Component configuration for a robust tunable sensor system for a high radiation environment

ABSTRACT

A method of capturing and analyzing information for a particle detection system comprises generating a reaction to a plurality of particles using a converter material, wherein the converter material is operable to interact with the plurality of particles. The method further comprises converting a response to the reaction to an electrical signal using a plurality of sensors, wherein the converter material is operable to be coated onto the plurality of sensors, and wherein each of the plurality of sensors comprises an array of discrete pixel sensors each with a respective (x,y) coordinate within the array. Further, the method comprises processing the electrical signal to generate data regarding each pixel on the array of discrete pixels and serializing the data collected from the plurality of sensors and transmitting the data over thin cables to a processing unit that is located at a separate and remote location from the plurality of sensors.

CROSS-REFERENCE TO RELATED APPLICATIONS Related Applications

This application is a conversion of and claims priority to and thebenefit of Provisional Patent Application No. 62/626,513, entitled“Subatomic Particle Detection System For Decommissioning Activities,”having a filing Date of Feb. 5, 2018, which is herein incorporated byreference in its entirety.

The present application is related to U.S. patent application Ser. No.13/894,305, filed May 14, 2013, now issued as U.S. Pat. No. 9,435,755,entitled “SCALABLE AND TUNABLE NEUTRON DETECTION INSTRUMENT,” namingAnshuman Roy as inventor. That application is incorporated herein byreference in its entirety and for all purposes.

The present application is related to U.S. patent application Ser. No.13/894,272, filed May 14, 2013, now issued as U.S. Pat. No. 9,435,897,entitled “TUNABLE DETECTION INSTRUMENT FOR SUBATOMIC PARTICLES,” namingAnshuman Roy as inventor. That application is incorporated herein byreference in its entirety and for all purposes.

The present application is related to U.S. patent application Ser. No.16/059,959, filed Aug. 9, 2018, entitled “PHYSICAL STRUCTURE FOR ATUNABLE SENSOR SYSTEM FOR PARTICLE DETECTION,” naming Anshuman Roy asinventor. That application is incorporated herein by reference in itsentirety and for all purposes.

The present application is related to U.S. patent application Ser. No.16/100,043, filed Aug. 9, 2018 entitled “METHOD AND APPARATUS FORPERFORMING PATTERN RECOGNITION FOR A TUNABLE SENSOR SYSTEM TO DETECTNEUTRON AND GAMMA PARTICLES,” naming Anshuman Roy as inventor. Thatapplication is incorporated herein by reference in its entirety and forall purposes.

FIELD OF THE INVENTION

Embodiments according to the present invention generally relate todetecting subatomic particles and more specifically to a device fordetecting subatomic particles.

BACKGROUND OF THE INVENTION

Neutrons are subatomic particles with no net electric charge. Neutronsand protons, another subatomic particle, together form the nucleus ofall elements in the periodic table except hydrogen. Free neutrons areproduced as a consequence of either nuclear fission, radioactive decayof elements or fusion. Special nuclear materials (“SNMs”) such asplutonium that are used for making dirty bombs decay radioactively toproduce neutrons. Detection of such neutrons is an effective way oftracking the source of SNMs. However, since neutrons do not carry anyelectric charge, their detection is problematic as compared to othercharged subatomic particles. One method of neutron detection that hasbeen successfully employed is to use materials that can capture incidentneutrons and convert them into other easily detectable subatomicparticles, such as alpha particles, tritons, gamma rays, etc.

Historically, high-pressure Helium-3 (He3) tubes have been the mainstayof neutron detection. Neutrons impinging on these tubes interact withHe3 nuclei to produce triton and protium, both of which areenergetically charged subatomic particles that migrate in the presenceof a strong electric field inside the tubes towards the electrodes.Unfortunately, He3 supplies on the planet are running low and the priceof He3 in recent years has increased twenty-fold in the last decadealone. Thus, there is a strong consensus in the field to replace He3technology with alternatives, mostly scintillation based detectionsystems and Boron lined proportional tubes.

Scintillator detectors also have several limitations. First,scintillation crystals are expensive and made in small volumes due to alimited market. Second, complicated pulse shape discriminationalgorithms need to be employed in these systems to discriminate neutronsfrom gamma rays, which also interact heavily with the scintillatingcrystals. Scintillation detectors also suffer from reliability issues onthe field due to the use of scintillating crystals that can be sensitiveto environmental factors such as humidity and salinity. The gammadiscrimination capability of Boron lined tubes is better thanscintillator detectors. However, being a proportional countertechnology, Boron lined tubes are limited by their form factor in thescope of their applications. Moreover, there is an absence of a globalsupply chain to drive down their cost over time. Both scintillation andproportional counter based systems must contend with significantsystem-level noise that interferes with measurements of low incidentneutron flux levels close to the cosmic background. They also lackmodularity, flexibility to detect subatomic particles other thanneutrons, and potential for rapid scalability.

Further, conventional methods of particle detection are typically toosensitive for extreme environmental conditions. For example, if aconventional particle detection system is being used to detect neutronsin a nuclear power plant or nuclear reactor after an accident, it maylikely destroy any of the known systems for radiation detection.

BRIEF SUMMARY OF THE INVENTION

Accordingly, what is needed is a technology for neutron detection thatemploys readily available and easily replaceable components that arereadily tunable to detect neutrons and designed to be modular. Further,the technology needs to be flexible so that other subatomic or otherparticles besides neutrons can also be detected. Moreover, thetechnology needs to be robust enough to detect radiation particle inintense radiation environments.

Disclosed herein is a modular and tunable technology platform comprisingsimple, easy-to-acquire, off-the-shelf components that are modified andassembled together to form a highly sensitive, high-performanceinstrument. The off-the-shelf components used to assemble the device maybe tuned to be sensitive to different particles including neutrons. Thereadily available and easily replaceable components of the presentinvention may be tuned to be sensitive to neutrons of differentenergies. The architecture of the embodiments of the invention disclosedherein not only allows for rapid, sensitive and flexible detection andimaging of neutrons, especially thermal neutrons, but also of a widevariety of other subatomic particles that may accompany neutrons thatoriginate from an SNM or other radioactive source. The systemarchitecture also enables identification of the element (radionuclide)that acts as a source of the incident neutrons. The architecture alsoenables tracking the direction of the source of neutrons andidentification of the radionuclide or non-radionuclide source from whichthe neutrons originated. Finally, the architectures of the embodimentsof the invention disclosed herein enable real time gamma discriminationthereby reducing false positives and response times of the instrument.

In one embodiment, a method for detecting particles is disclosed. Themethod comprises generating a reaction to a plurality of particles usinga converter material, wherein the converter material is operable tointeract with the plurality of particles, and wherein a subset of theplurality of particles comprises neutrons. Further, the method comprisesconverting a response to the reaction to a readable electrical signalusing a sensor, wherein the sensor comprises an array of pixels. Also,the method comprises processing the readable electrical signal from thesensor to generate information for each pixel on the array of pixels andtransmitting the information to a processing unit. Finally, the methodcomprises executing a discrimination procedure using the information fordistinguishing between instances of impingement of neutrons andinstances of impingement of non-neutron particles on the array ofpixels.

In one embodiment, an apparatus for detecting neutrons is disclosed. Theapparatus comprises a converter layer operable to interact with andgenerate a reaction to a plurality of particles, wherein a subset of theplurality of particles comprises neutrons. It also comprises a sensorcoupled to the converter layer, wherein the sensor is operable toconvert a response to the reaction to a readable electrical signal, andwherein the sensor comprises an array of discrete pixel sensors eachwith a respective (x,y) coordinate within the array. The apparatusfurther comprises a first processing device operable to process thereadable electrical signal to generate information for each pixel on thearray and a second processing device communicatively coupled to thefirst processing device. The second processing device is configured to:(a) control the first processing device; (b) receive the informationfrom the first processing device; and (c) execute a discriminationprocedure using the information to distinguish between instances ofimpingement of neutrons and instances of impingement of non-neutronparticles on the array.

In one embodiment, a system for detecting neutrons is disclosed. Thesystem comprises a plurality of sensor modules, wherein each sensormodule comprises a plurality of sensor elements and a first processingdevice. Each of the sensor elements comprises at least one converterlayer operable to interact with and generate a reaction to a pluralityof particles, wherein a subset of the plurality of particles comprisesneutrons. Each sensor element also comprises a sensor coupled to the atleast one converter layer, wherein the sensor is operable to convert aresponse to the reaction to a readable electrical signal. Further, thesensor comprises an array of discrete pixel sensors each with arespective (x,y) coordinate within the array. The system can alsocomprise a second processing device communicatively coupled to theplurality of sensor modules, wherein the second processing device isoperable to read information regarding a respective readable electricalsignal from a respective first processing device on each of theplurality of sensor modules. Further, the second processing device isoperable to execute a discrimination procedure using the information todistinguish between instances of impingement of neutrons and instancesof impingement of non-neutron particles on respective arrays of pixelsensors associated with the plurality of sensor modules. Finally, thesystem comprises a housing to encapsulate the plurality of sensormodules, wherein at least one of the plurality of sensor modules istuned to detect a neutron and at least one of the plurality of sensormodules is tuned to detect a non-neutron particle.

In one embodiment, a sensor for detecting particles is disclosed. Thesensor comprises a silicon wafer substrate and a charge detection layerdisposed on the silicon wafer substrate, wherein the charge detectionlayer comprises a plurality of discrete pixel sensors. The sensor alsocomprises a converter material operable to interact with particles of afirst type to generate a reaction, wherein the reaction produces chargedparticles, wherein the charge detection layer is configured to detectcharged particles produced by the reaction, and wherein the chargedetection layer is configured to generate a readable electrical signalwith information regarding the charged particles detected. Further, thesensor comprises a substrate layer operable to filter particles of asecond type, wherein the converter material is coated on an underside ofthe substrate layer wherein the converter material faces the chargedetection layer and an air gap is formed between the converter materialand the charge detection layer.

In a different embodiment, a sensor for detecting particles isdisclosed. The sensor comprises a silicon wafer substrate and a chargedetection layer disposed on the silicon wafer substrate, wherein thecharge detection layer comprises a plurality of discrete pixel sensors.The sensor further comprises a converter material operable to interactwith a particles of a first type to generate a reaction, wherein thereaction produces charged particles, wherein the charge detection layeris configured to detect charged particles produced by the reaction, andwherein the charge detection layer is configured to generate a readableelectrical signal with information regarding the charged particlesdetected. Also, the sensor comprises a substrate layer operable tocondition particles of a second type, wherein an interaction with thesubstrate layer changes a characteristic of particles of the secondtype, wherein the converter material is coated on an underside of thesubstrate layer wherein the converter material faces the chargedetection layer and an air gap is formed between the converter materialand the charge detection layer.

In one embodiment, a sensor for detecting particles is disclosed wherethe sensor comprises a silicon wafer substrate and a a charge detectionlayer disposed on the silicon wafer substrate, wherein the chargedetection layer comprises a plurality of discrete pixel sensors. Thesensor further comprises a converter material operable to interact withone or more types of particles to generate a reaction, wherein thereaction produces charged particles, wherein the charge detection layeris configured to detect charged particles produced by the reaction, andwherein the charge detection layer is configured to generate a readableelectrical signal with information regarding the charged particlesdetected. Also, the sensor comprises a substrate layer operable tofilter a type of particles different from the one or more types ofparticles that interact with the converter material, wherein thesubstrate layer is adjacent to the converter material and on an oppositeside from the charge detection layer.

In one embodiment, a method of capturing and analyzing information for aparticle detection system is disclosed. The method comprising generatinga reaction to a plurality of particles using a converter material,wherein the converter material is operable to interact with theplurality of particles. The method further comprises converting aresponse to the reaction to an electrical signal using a plurality ofsensors, wherein the converter material is operable to be coated ontothe plurality of sensors, and wherein each of the plurality of sensorscomprises an array of discrete pixel sensors each with a respective(x,y) coordinate within the array. Further, the method comprisesprocessing the electrical signal to generate data regarding each pixelon the array of discrete pixels and serializing the data collected fromthe plurality of sensors and transmitting the data over thin cables to aprocessing unit, wherein the processing unit is located at a separateand remote location from the plurality of sensors. Finally, the methodcomprises converting the data into a sequence of images comprising avisual representation of the plurality of particles impinging on theplurality of sensors.

In one embodiment, an apparatus for capturing and analyzing informationfor a particle detection system is disclosed. The apparatus comprises aconverter layer coated onto a substrate and operable to interact withand generate a reaction to a plurality of particles, wherein theplurality of particles comprise neutrons. The apparatus also comprises aplurality of sensors in proximity to and facing the converter layer,wherein the plurality of sensors is operable to convert a response tothe reaction to an electrical signal, and wherein the sensor comprisesan array of discrete pixel sensors. Furthermore, the apparatus comprisesa first processing device operable to process the electrical signal togenerate information for each pixel on the array of discrete pixelsensors, a data serializer to serialize the information generated, andtransmission line cables for transmitting the information to a secondprocessing unit, wherein the second processing unit is located at aseparate and remote location from the plurality of sensors. The secondprocessing device is communicatively coupled to the first processingdevice, and the second processing device is configured to: a) controlthe first processing device; b) receive the information from the firstprocessing device; and c) convert the information into a sequence ofimages comprising a visual representation of the plurality of particlesimpinging on the plurality of sensors.

In an embodiment, a system for detecting neutrons is disclosed. Thesystem comprises a plurality of sensor arrays, wherein each sensor arraycomprises a plurality of sensors, wherein each sensor comprises: a) aconverter layer disposed on the sensor, wherein the converter layer isoperable to interact with and generate a reaction to a plurality ofparticles, wherein the plurality of particles comprises neutrons; b) anarray of discrete pixel sensors each with a respective (x,y) coordinatewithin the array, wherein the discrete pixel sensors are operable toconvert a response to the reaction to a readable electrical signal; c) afirst processing device operable to process the readable electricalsignal to generate information for each pixel on the array of discretepixel sensors; and d) a data serializer to serialize the information.The system also comprises a plurality of second processing devicescommunicatively coupled to the plurality of sensors, wherein each secondprocessing device is associated with a discrete one of the plurality ofsensors, wherein each second processing device is operable to receivethe serialized information from an associated sensor using thin cables,and wherein the plurality of second processing devices are located at aseparate and remote location from the plurality of sensors arrays.

In one embodiment, a system for detecting neutrons is disclosed. Thesystem comprises a plurality of sensor arrays, wherein each sensor arraycomprises a plurality of sensors, wherein each sensor comprises: a) aconverter layer disposed on the sensor, wherein the converter layer isoperable to interact with and generate a reaction to a plurality ofparticles, wherein the plurality of particles comprises neutrons; b) anarray of discrete pixel sensors each with a respective (x,y) coordinatewithin the array, wherein the discrete pixel sensors are operable toconvert a response to the reaction to a readable electrical signal; c) afirst processing device operable to process the readable electricalsignal to generate information for each pixel on the array of discretepixel sensors; and d) a data serializer to serialize the information,wherein the first processing device and the data serializer are locatedin proximity to a respective sensor. The system also comprises aplurality of second processing devices communicatively coupled to theplurality of sensors, wherein each second processing device isassociated with a discrete one of the plurality of sensors, wherein eachsecond processing device is operable to receive the serializedinformation from an associated sensor using thin cables, wherein theplurality of second processing devices are located at a separate andremote location from the plurality of sensors arrays, and wherein theplurality of second processing device are operable to detect a particletype based on the serialized information received from the plurality ofsensor arrays.

In a different embodiment, a computer implemented method of detectingneutrons in images from a tunable sensor system is disclosed. The methodcomprises training a deep learning process to recognize knownradiation-dependent signature patterns created by neutrons in testimages. The method further comprises splitting an input image into aplurality of frames and passing the plurality of frames through the deeplearning process in order to recognize neutrons in the plurality offrames. Subsequently, the method comprises recombining the plurality offrames back into the input image. For each pixel within the input image,the method comprises examining pixels connected to a respective pixel todetermine if a signature pattern particular to neutrons is presentwithin the input image and counting a number of neutrons within theinput image using results from the examining.

In another embodiment, a system for detecting neutrons in images from atunable sensor system is disclosed. The system comprises a memory forstoring a plurality of test images, an input image, and instructionsassociated with a deep learning process and a process for detectingparticles of interest in images. The system also comprises a processorcoupled to the memory, the processor being configured to operate inaccordance with the instructions to: a) train the deep learning processto recognize known radiation-dependent signature patterns created by aparticle of interest in test images; b) split an input image into aplurality of frames; c) pass the plurality of frames through the deeplearning process in order to recognize the particle of interest in theplurality of frames; d) combine the plurality of frames back into theinput image; e) for each pixel within the input image, examine pixelsconnected to a respective pixel to determine if a signature patternparticular to the particle of interest is present within the inputimage; and f) determine a count of the particle of interest within theinput image using the connected pixels.

The following detailed description together with the accompanyingdrawings will provide a better understanding of the nature andadvantages of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention are illustrated by way of example,and not by way of limitation, in the figures of the accompanyingdrawings and in which like reference numerals refer to similar elements.

FIG. 1 is a block diagram of an example of a computing system capable ofimplementing embodiments of the present disclosure.

FIG. 2 is a block diagram of an example of a network architecture inwhich client systems and servers may be coupled to a network, accordingto embodiments of the present invention.

FIG. 3 is an exemplary block diagram of a subatomic particle detectionsystem in accordance with one embodiment of the present invention.

FIG. 4 is a schematic block diagram illustrating a typical hardwareconfiguration for connecting the host machine with the sensor modules inaccordance with one embodiment of the present invention.

FIG. 5A is an exemplary block diagram of a sensor array of pixels inaccordance with one embodiment of the present invention.

FIG. 5B is an exemplary block diagram illustrating the cross-sectionalview for each pixel in accordance with one embodiment of the presentinvention.

FIGS. 6A and 6B illustrate two exemplary patterns created by twodifferent types of subatomic particles and as detected by a pixel arrayof sensors in accordance with one embodiment of the invention.

FIG. 7 illustrates an exemplary information vector created for eachpixel by the MPU in accordance with one embodiment of the presentinvention.

FIG. 8 depicts a flowchart of an exemplary computer implemented processof detecting subatomic particles, according to an embodiment of thepresent invention.

FIG. 9 illustrates exemplary signatures for neutron and gamma particlesused to distinguish between the two particles in accordance with oneembodiment of the present invention.

FIG. 10 illustrates the physical architecture of a sensor in accordancewith an embodiment of the present invention.

FIG. 11 is an exemplary diagram of a cross-section of a nuclear primarycontainment vessel (PCV) of a reactor that may contain nuclear sedimentresulting from a nuclear accident.

FIG. 12 illustrates the manner in which the sensing elements can beconfigured in a stack formation in accordance with an embodiment of thepresent invention.

FIG. 13 illustrates the manner in which the sensing elements can beconfigured in a cubical formation in accordance with an embodiment ofthe present invention.

FIG. 14 illustrates the manner in which a sensor cubes can be made tofit within a cylindrical sensor head in accordance with an embodiment ofthe present invention.

FIG. 15 illustrates the manner in which multiple sensor cubes can beconfigured to fit within a cylindrical sensor head in accordance with anembodiment of the present invention.

FIG. 16A illustrates the various configurations that sensors can beplaced in to maximize sensitivity in accordance with embodiments of thepresent invention.

FIG. 16B illustrates a collimated configuration that can be used toimprove directional accuracy in accordance with an embodiment of thepresent invention.

FIG. 17 illustrates a detector configured in the shape of a cube that isused to generate a debris map in accordance with an embodiment of thepresent invention.

FIG. 18 illustrates the manner in which multiple cubed sensors can beused to enable more efficient debris mapping in accordance with anembodiment of the present invention.

FIG. 19A illustrates a cylindrical configuration that enables multiplesensors to be stacked to increase sensitivity in accordance with anembodiment of the present invention.

FIG. 19B illustrates another type of cylindrical configuration thatenables multiple sensors to be stacked to increase sensitivity inaccordance with an embodiment of the present invention.

FIG. 19C illustrates a type of cylindrical configuration that uses aneutron block to increase directional sensitivity in accordance with anembodiment of the present invention.

FIG. 20 illustrates the manner in which CMOS device sensors and PINdiode sensors can be combined in the same detector system in accordancewith embodiments of the present invention.

FIG. 21A is a logical diagram that illustrates the manner in which datais transmitted from the sensors to the command and control equipment inaccordance with an embodiment of the present invention.

FIG. 21B is a logical diagram that illustrates the manner in which datais transmitted from a robot in a nuclear primary containment vessel(PCV) of a reactor to a safe room with the command and control equipmentin accordance with an embodiment of the present invention.

FIG. 22 illustrates the manner in which the sensor for the detector isseparated from the additional electronics in accordance with anembodiment of the present invention.

FIG. 23A illustrates the sensor-level measurement flow diagram and themanner in which neutron and gamma counts are output from the individualsensors and processed in accordance with an embodiment of the presentinvention.

FIG. 23B illustrates is a flow diagram illustrating the manner in whichsensor information is processed and outputted by the two different typesof neural networks in accordance with an embodiment of the presentinvention.

FIG. 24A illustrates an exemplary output of a PIN diode from whichneutrons can be identified using the analog pulse neural network inaccordance with an embodiment of the present invention.

FIG. 24B illustrates an exemplary output of a CMOS sensor from whichneutrons can be identified using digital pattern neural network thatanalyzes sensor information from CMOS sensors in accordance with anembodiment of the present invention.

FIG. 25A illustrates representative frames from CMOS radiation sensorsin response to varying levels of gamma radiation in accordance with anembodiment of the present invention.

FIG. 25B illustrates representative frames at the pixel level from CMOSradiation sensors in response to varying levels of gamma radiation inaccordance with an embodiment of the present invention.

FIG. 25C illustrates histograms of the representative images from FIGS.25A and 25B in accordance with an embodiment of the present invention.

FIG. 26A illustrates a collection of eight bright neutron counts with abackground gamma radiation of 0 Gy/hr in accordance with an embodimentof the present invention.

FIG. 26B illustrates a magnified view of a count comprising at least 4saturated pixels.

FIG. 27A illustrates a first pixel level image with neutron and gammasignatures in the same image in accordance with an embodiment of thepresent invention.

FIG. 27B illustrates a second pixel level image with neutron and gammasignatures in the same image in accordance with an embodiment of thepresent invention.

FIG. 28 illustrates pixel level images with neutron counts under highgamma conditions in accordance with an embodiment of the presentinvention.

FIG. 29 depicts a flowchart of an exemplary computer implemented processfor detecting the presence of neutrons in images produced from sensorinformation in accordance with an embodiment of the present invention.

FIG. 30 depicts a flowchart of an exemplary computer implemented processfor analyzing images to detect neutrons using deep learning processes inaccordance with an embodiment of the present invention.

FIG. 31 depicts a flowchart of an exemplary computer implemented processfor triangulating a source location for neutron particles in accordancewith an embodiment of the present invention.

FIG. 32 depicts a flowchart of an exemplary computer implemented processfor independently controlling sensors in order to ensure reliability inaccordance with an embodiment of the present invention.

FIG. 33 depicts a flowchart of an exemplary computer implemented processfor gathering information from tunable sensors used for particledetection in accordance with an embodiment of the present invention.

FIG. 34 depicts a flowchart of an exemplary computer implemented processfor disabling sensors that are not functioning in order to ensurereliability of the detector and increase an operational life of adetector in accordance with an embodiment of the present invention.

FIG. 35 depicts a flowchart of an exemplary computer implemented processfor conserving power and managing heat in a tunable detector system inaccordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made in detail to the various embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. While described in conjunction with theseembodiments, it will be understood that they are not intended to limitthe disclosure to these embodiments. On the contrary, the disclosure isintended to cover alternatives, modifications and equivalents, which maybe included within the spirit and scope of the disclosure as defined bythe appended claims. Furthermore, in the following detailed descriptionof the present disclosure, numerous specific details are set forth inorder to provide a thorough understanding of the present disclosure.However, it will be understood that the present disclosure may bepracticed without these specific details. In other instances, well-knownmethods, procedures, components, and circuits have not been described indetail so as not to unnecessarily obscure aspects of the presentdisclosure.

Some portions of the detailed descriptions that follow are presented interms of procedures, logic blocks, processing, and other symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the means used by thoseskilled in the data processing arts to most effectively convey thesubstance of their work to others skilled in the art. In the presentapplication, a procedure, logic block, process, or the like, isconceived to be a self-consistent sequence of steps or instructionsleading to a desired result. The steps are those utilizing physicalmanipulations of physical quantities. Usually, although not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated in a computer system. It has proven convenient at times,principally for reasons of common usage, to refer to these signals astransactions, bits, values, elements, symbols, characters, samples,pixels, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout the present disclosure,discussions utilizing terms such as “generating,” “converting,”“processing,” “analyzing,” “transmitting,” “allocating,” “detecting,”“associating,” “accessing,” “erasing,” “freeing,” “controlling,”“determining,” “identifying,” or the like, refer to actions andprocesses (e.g., flowchart 800 of FIG. 8) of a computer system orsimilar electronic computing device or processor (e.g., computing system110 of FIG. 1). The computer system or similar electronic computingdevice manipulates and transforms data represented as physical(electronic) quantities within the computer system memories, registersor other such information storage, transmission or display devices.

Embodiments described herein may be discussed in the general context ofcomputer-executable instructions residing on some form ofcomputer-readable storage medium, such as program modules, executed byone or more computers or other devices. By way of example, and notlimitation, computer-readable storage media may comprise non-transitorycomputer-readable storage media and communication media; non-transitorycomputer-readable media include all computer-readable media except for atransitory, propagating signal. Generally, program modules includeroutines, programs, objects, components, data structures, etc., thatperform particular tasks or implement particular abstract data types.The functionality of the program modules may be combined or distributedas desired in various embodiments.

Computer storage media includes volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules or other data. Computer storage media includes, but isnot limited to, random access memory (RAM), read only memory (ROM),electrically erasable programmable ROM (EEPROM), flash memory or othermemory technology, compact disk ROM (CD-ROM), digital versatile disks(DVDs) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium that can be used to store the desired information and that canaccessed to retrieve that information.

Communication media can embody computer-executable instructions, datastructures, and program modules, and includes any information deliverymedia. By way of example, and not limitation, communication mediaincludes wired media such as a wired network or direct-wired connection,and wireless media such as acoustic, radio frequency (RF), infrared, andother wireless media. Combinations of any of the above can also beincluded within the scope of computer-readable media.

FIG. 1 is a block diagram of an example of a computing system 110 for aneutron and other subatomic particles detecting system capable ofimplementing embodiments of the present disclosure. Computing system 110broadly represents any single or multi-processor computing device orsystem capable of executing computer-readable instructions. Examples ofcomputing system 110 include, without limitation, workstations, laptops,client-side terminals, servers, distributed computing systems, handhelddevices, or any other computing system or device. In its most basicconfiguration, computing system 110 may include at least one processor114 and a system memory 116.

Processor 114 generally represents any type or form of processing unitcapable of processing data or interpreting and executing instructions.In certain embodiments, processor 114 may receive instructions from asoftware application or module. These instructions may cause processor114 to perform the functions of one or more of the example embodimentsdescribed and/or illustrated herein.

System memory 116 generally represents any type or form of volatile ornon-volatile storage device or medium capable of storing data and/orother computer-readable instructions. Examples of system memory 116include, without limitation, RAM, ROM, flash memory, or any othersuitable memory device. Although not required, in certain embodimentscomputing system 110 may include both a volatile memory unit (such as,for example, system memory 116) and a non-volatile storage device (suchas, for example, primary storage device 132).

Computing system 110 may also include one or more components or elementsin addition to processor 114 and system memory 116. For example, in theembodiment of FIG. 1, computing system 110 includes a memory controller118, an input/output (I/O) controller 120, and a communication interface122, each of which may be interconnected via a communicationinfrastructure 112. Communication infrastructure 112 generallyrepresents any type or form of infrastructure capable of facilitatingcommunication between one or more components of a computing device.Examples of communication infrastructure 112 include, withoutlimitation, a communication bus (such as an Industry StandardArchitecture (ISA), Peripheral Component Interconnect (PCI), PCI Express(PCIe), or similar bus and a network.

Memory controller 118 generally represents any type or form of devicecapable of handling memory or data or controlling communication betweenone or more components of computing system 110. For example, memorycontroller 118 may control communication between processor 114, systemmemory 116, and I/O controller 120 via communication infrastructure 112.

I/O controller 120 generally represents any type or form of modulecapable of coordinating and/or controlling the input and outputfunctions of a computing system 110. For example, I/O controller 120 maycontrol or facilitate transfer of data between one or more elements ofcomputing system 110, such as processor 114, system memory 116,communication interface 122, display adapter 126, input interface 130,and storage interface 134.

Communication interface 122 broadly represents any type or form ofcommunication device or adapter capable of facilitating communicationbetween example computing system 110 and one or more additional devices.For example, communication interface 122 may facilitate communicationbetween computing system 110 and a private or public network includingadditional computing systems. Examples of communication interface 122include, without limitation, a wired network interface (such as anetwork interface card), a wireless network interface (such as awireless network interface card), a modem, and any other suitableinterface. In one embodiment, communication interface 122 provides adirect connection to a remote server via a direct link to a network,such as the Internet. Communication interface 122 may also indirectlyprovide such a connection through any other suitable connection.

Communication interface 122 may also represent a host adapter configuredto facilitate communication between computing system 110 and one or moreadditional network or storage devices via an external bus orcommunications channel. Examples of host adapters include, withoutlimitation, Small Computer System Interface (SCSI) host adapters,Universal Serial Bus (USB) host adapters, IEEE (Institute of Electricaland Electronics Engineers) 1394 host adapters, Serial AdvancedTechnology Attachment (SATA) and External SATA (eSATA) host adapters,Advanced Technology Attachment (ATA) and Parallel ATA (PATA) hostadapters, Fibre Channel interface adapters, Ethernet adapters, or thelike. Communication interface 122 may also allow computing system 110 toengage in distributed or remote computing. For example, communicationinterface 122 may receive instructions from a remote device or sendinstructions to a remote device for execution.

As illustrated in FIG. 1, computing system 110 may also include at leastone display device 124 coupled to communication infrastructure 112 via adisplay adapter 126. Display device 124 generally represents any type orform of device capable of visually displaying information forwarded bydisplay adapter 126. Similarly, display adapter 126 generally representsany type or form of device configured to forward graphics, text, andother data for display on display device 124.

As illustrated in FIG. 1, computing system 110 may also include at leastone input device 128 coupled to communication infrastructure 112 via aninput interface 130. Input device 128 generally represents any type orform of input device capable of providing input, either computer- orhuman-generated, to computing system 110. Examples of input device 128include, without limitation, a keyboard, a pointing device, a speechrecognition device, or any other input device.

As illustrated in FIG. 1, computing system 110 may also include aprimary storage device 132 and a backup storage device 133 coupled tocommunication infrastructure 112 via a storage interface 134. Storagedevices 132 and 133 generally represent any type or form of storagedevice or medium capable of storing data and/or other computer-readableinstructions. For example, storage devices 132 and 133 may be a magneticdisk drive (e.g., a so-called hard drive), a floppy disk drive, amagnetic tape drive, an optical disk drive, a flash drive, or the like.Storage interface 134 generally represents any type or form of interfaceor device for transferring data between storage devices 132 and 133 andother components of computing system 110.

In one example, databases 140 may be stored in primary storage device132. Databases 140 may represent portions of a single database orcomputing device or it may represent multiple databases or computingdevices. For example, databases 140 may represent (be stored on) aportion of computing system 110 and/or portions of example networkarchitecture 200 in FIG. 2 (below). Alternatively, databases 140 mayrepresent (be stored on) one or more physically separate devices capableof being accessed by a computing device, such as computing system 110and/or portions of network architecture 200.

Continuing with reference to FIG. 1, storage devices 132 and 133 may beconfigured to read from and/or write to a removable storage unitconfigured to store computer software, data, or other computer-readableinformation. Examples of suitable removable storage units include,without limitation, a floppy disk, a magnetic tape, an optical disk, aflash memory device, or the like. Storage devices 132 and 133 may alsoinclude other similar structures or devices for allowing computersoftware, data, or other computer-readable instructions to be loadedinto computing system 110. For example, storage devices 132 and 133 maybe configured to read and write software, data, or othercomputer-readable information. Storage devices 132 and 133 may also be apart of computing system 110 or may be separate devices accessed throughother interface systems.

Many other devices or subsystems may be connected to computing system110. Conversely, all of the components and devices illustrated in FIG. 1need not be present to practice the embodiments described herein. Thedevices and subsystems referenced above may also be interconnected indifferent ways from that shown in FIG. 1. Computing system 110 may alsoemploy any number of software, firmware, and/or hardware configurations.For example, the example embodiments disclosed herein may be encoded asa computer program (also referred to as computer software, softwareapplications, computer-readable instructions, or computer control logic)on a computer-readable medium.

The computer-readable medium containing the computer program may beloaded into computing system 110. All or a portion of the computerprogram stored on the computer-readable medium may then be stored insystem memory 116 and/or various portions of storage devices 132 and133. When executed by processor 114, a computer program loaded intocomputing system 110 may cause processor 114 to perform and/or be ameans for performing the functions of the example embodiments describedand/or illustrated herein. Additionally or alternatively, the exampleembodiments described and/or illustrated herein may be implemented infirmware and/or hardware.

A computer program for controlling the particle detection system may bestored on the computer readable medium and then stored in system memory116 and/or various portions of storage devices 132 and 133. Whenexecuted by the processor 114, the computer program may cause theprocessor 114 to perform and/or be a means for performing the functionsrequired for carrying out particle detection.

FIG. 2 is a block diagram of an example of a network architecture 200 inwhich client systems 210, 220, and 230 and servers 240 and 245 may becoupled to a network 250. Client systems 210, 220, and 230 generallyrepresent any type or form of computing device or system, such ascomputing system 110 of FIG. 1.

Similarly, servers 240 and 245 generally represent computing devices orsystems, such as application servers or database servers, configured toprovide various database services and/or run certain softwareapplications. Network 250 generally represents any telecommunication orcomputer network including, for example, an intranet, a wide areanetwork (WAN), a local area network (LAN), a personal area network(PAN), or the Internet.

With reference to computing system 110 of FIG. 1, a communicationinterface, such as communication interface 122, may be used to provideconnectivity between each client system 210, 220, and 230 and network250. Client systems 210, 220, and 230 may be able to access informationon server 240 or 245 using, for example, a Web browser or other clientsoftware. Such software may allow client systems 210, 220, and 230 toaccess data hosted by server 240, server 245, storage devices260(1)-(L), storage devices 270(1)-(N), storage devices 290(1)-(M), orintelligent storage array 295. Although FIG. 2 depicts the use of anetwork (such as the Internet) for exchanging data, the embodimentsdescribed herein are not limited to the Internet or any particularnetwork-based environment.

In one embodiment, all or a portion of one or more of the exampleembodiments disclosed herein are encoded as a computer program andloaded onto and executed by server 240, server 245, storage devices260(1)-(L), storage devices 270(1)-(N), storage devices 290(1)-(M),intelligent storage array 295, or any combination thereof. All or aportion of one or more of the example embodiments disclosed herein mayalso be encoded as a computer program, stored in server 240, run byserver 245, and distributed to client systems 210, 220, and 230 overnetwork 250.

Scalable and Tunable Neutron Detection Instrument

Embodiments of the present invention provide methods and systems fordetecting neutrons and other subatomic particles. While the discussionbelow predominantly focuses on subatomic particles including neutrons,embodiments and principles of the present invention can also be used todetect atomic species, e.g., ions, gases, etc. or molecular species aswell.

Disclosed herein is a modular and tunable technology platform comprisingreadily available, easy-to-acquire, off-the-shelf components that areassembled together to form a highly sensitive, robust, high-performanceinstrument. The off-the-shelf components used to assemble the device maybe tuned to be sensitive to different particles. The architecture of theinvention disclosed herein allows for rapid, sensitive and flexibledetection and identification of a wide variety of subatomic particlessuch as neutrons, gamma rays, beta particles, alpha particles,neutrinos, muons, etc. using the same instrument. Also, the particledetection device of embodiments of the present invention can be designedusing solid-state electronics which helps reduce noise and vibration.

FIG. 3 is an exemplary block diagram of a subatomic particle detectionsystem in accordance with one embodiment of the present invention. Thesubatomic particle detection system can have a hierarchical architecturecomprising elements and modules that are arranged in a configurationspecific to the application. FIG. 3 illustrates a subatomic particledetection system comprising “N” number of elements, from element E1 320to element En 325. These elements comprise the basic building blocks ofthe subatomic particle detection system. Each sensor module 330 withinthe subatomic particle detection system may comprise hundreds or eventhousands of elements. Accordingly, the number of elements “N” may onlybe bounded by practical considerations.

As shown in FIG. 3, in one embodiment, each element may have a converterlayer, C1 390, that interacts with incident subatomic particles. In oneembodiment, converter layer, C1 390, can be a thin film material thatcan be applied directly to the sensor. The design of the subatomicparticle detection system is tunable because different materials(referred to herein interchangeably as “converter materials” or“reactive materials”) can be used to develop C1 390 so as to make theelements sensitive to different subatomic particles.

For example, in one embodiment, the subatomic particle detection systemmay be tuned for neutron detection by making C1 a layer of reactiveneutron-capturing materials such as ¹⁵⁷Gadolinium (also known as “nativeGadolinium” which is a mixture of several isotopes including¹⁵⁷Gadolinium), ¹⁰Boron (also known as native Boron), ⁶Lithium (alsoknown as native Lithium), etc. These converter layers may be in pureelemental form or in compound form or be a mixture of elements andcompounds of neutron absorbing isotopes of elements in any combination.In a different embodiment, the subatomic particle detection system maybe tuned for gamma-ray detection by developing C1 with a reactivematerial that interacts with gamma rays such as Cesium Iodide, SodiumIodide, etc. In another embodiment, the subatomic particle detectionsystem may be tuned for fast neutron detection by designing C1 with alayer of polyethylene, paraffin wax, any compound from the epoxy orsilicone families, or other such hydrogenous material. In oneembodiment, C1 can be designed to be placed in conjunction with ahydrogenous material as well.

In another embodiment, one or more groups of elements may be coated withdifferent converter layers to make the subatomic particle detectionsystem sensitive to multiple types of particles simultaneously. Forexample, in one application related to utilities, a utility company mayneed a single sensor that can simultaneously detect neutron, gamma,alpha and beta particles. Instead of using four different types ofdetectors to detect these particles separately, which was done in thepast, embodiments of the present invention can have a single detectorwith four sensors, where each sensor detects one of the four types ofparticles. Alternatively, embodiments of the present invention, allow asingle sensor to be partitioned in multiple ways, where each partitioncan detect a different type of particle because each partition may becoated with a different converter layer.

In one embodiment, C1 can be selected from the following: Xenon,Cadmium, Hafnium, Gadolinium, Cobalt, Samarium, Lithium, Titanium,Europium, Molybdenum, Ytterbium, Dysprosium, Erbium, and Boron in theirnative form, or isotope enriched form, as well as compounds from theforegoing list in their native or isotope enriched forms, such as butnot limited to oxides, carbides, halides (e.g., iodides, chlorides, toname a few), etc. as well as combinations of the elements in ablend/alloy form or compounds of such combinations, such as GadoliniumTitanate, Boron Carbide, DiMolybdenum Pentaboride (Mo₂B₅) etc.

The converter layers may be deposited by vapor state, liquid state orplasma state deposition techniques. In one manifestation, the converterlayer used can be a fullerene compound of lithium, C60AxLix, that isdeposited from solution state using a combination of solvents such aschlorobenzene and dichlorobenzene. In another embodiment, modified Boronfullerenes can be deposited from the solution state. In one embodiment,converter layer can also be nanotube or graphene compound (made of anymaterial comprising a molecule from the carbon based fullerene familysuch as C₆₀, C₇₀, C₈₄ etc.) chemically attached or bonded to a neutronabsorbing element or compound. The carbon based fullerene molecule inthis case can be chemically bonded to a neutron absorbing molecule,either inside (endohedral fullerenes) or outside the fullerene cage. Thefullerene molecule may also be made with Boron, such as Boron Fullerene,in which case there is no need for a carbon based fullerene molecule.

In one embodiment, each element may also comprise a sensor array ofpixels, P1 315. The pixels in the sensor array of pixels P1 315 convertsparticles, e.g., products of the interaction between the incidentsubatomic particles and C1 390 (the converter layer discussed above), toan electrical output that may be converted from analog to a digitalsignal through a combination of transistors and analog to digitalconverters at the pixel level or separately. In one embodiment, thesensor array responds to light or charge energy produced in the coating,which is then detected by the sensor pixels. These transistors andanalog to digital converters may reside in a control electronics module310, wherein each element comprises its own control electronics module310. In one embodiment, sensor array P1 315 may be an off-the-shelfsensor. The sensor, for example, among other things, could be amemristor or an image sensor or a photon detector or a photovoltaiccell. The sensor could also be a type of sensor commonly used inconventional consumer electronic device digital cameras.

In one embodiment, the sensor array of pixels P1 315 is made from anymaterial that can detect charged particles, some examples of whichinclude semiconducting polymers, e.g., Poly(3-hexylthiphene),Poly[[9-(1-octylnonyl)-9H-carbazole-2,7-diyl]-2,5-thiophenediyl-2,1,3-benzothiadiazole-4,7-diyl-2,5-thiophenediyl]also known as PCDTBT, etc., small organic semiconducting molecules, orinorganic semiconductors such as silicon, Cadmium Telluride, CadmiumZinc Telluride, etc., or compound semiconductors such as GalliumNitride, Gallium Indium Arsenide, or liquid state semiconductingmaterials, or any other material (solid, liquid or gas) that can sense(e.g., by detecting light or charge) products of interaction between C1and incident subatomic particles, including neutrons.

In one embodiment, C1 390 may also comprise multiple layers of materialsthat interact with different subatomic particles, including neutrons ofdifferent energies or other subatomic particles like gamma rays, or itmay be a composite of various materials, each of which interacts with adifferent subatomic particle, or it may be a combination of the twoapproaches.

Further, the presence of C1 390 does not preclude the possibility ofincident subatomic particles, including neutrons, interacting directlywith materials comprising the sensors. For example, in one embodiment,there may be instances where the materials forming the sensor pixelarray are themselves sensitive to the incident subatomic particles, suchas silicon is sensitive to gamma rays, muons, etc. or Boron used forp-type doping of silicon is sensitive to neutrons. Further, by way ofexample, a semiconductor such as silicon may be doped with high neutroncapture cross section material such as 157Gd. Also, a semiconductor suchas PCBM (fullerene derivative [6,6]-phenyl-C61-butyric acid methylester) may be modified chemically with neutron capture materials torender the molecules neutron-sensitive.

In one embodiment, the particle detection system may not include aconverter layer C1 390 at all. Instead, converter material that wouldotherwise be used to create the C1 layer 390 is homogeneously intermixedwith the sensor material used to create pixel array, P1 315. By way ofexample, compounds of neutron capturing material may be intermixed withsensor materials such as semiconducting polymers, e.g., P3HT, PCDTBT,etc., small organic semiconducting molecules, or inorganicsemiconductors such as Silicon, CdTe, etc. or compound semiconductorssuch as Gallium Nitride, Gallium Indium Arsenide, or liquid statesemiconducting materials. Further, P1 315 may comprise a pixilated oruniform sensory array (or monolithic sensor array) made fromsemiconducting materials or materials sensitive to the products of theinteraction between incident subatomic particles and the reactivematerials. Also it may comprise composite materials sensitive tosubatomic particles and capable of generating a readable signal.

Dispersing the converter material within the sensor material, however,may require printing technology. Also, special processes would berequired to intermix the converter material with the sensor. Asdiscussed above, control electronics module 310 can be used forcontrolling the operation of the element and transmitting any analog ordigital signal generated by the element to the remainder of the system.

In one embodiment, each of the elements E1 320 through En 325, maycomprise a lensing apparatus L1 305 for focusing the particles towardsthe sensor with the intent of improving the instrument's sensitivity.For example, if the particle detection system is set up for detectingneutrons, the neutrons can be lensed using appropriate materials such asglass poly-capillary fibers made from lead-silica glass and used forfocusing ultra-cold to fast neutrons. Alternatively, if the particledetection system is set up for detecting X-rays, the X-rays can belensed using appropriate materials such as microstructured capillaryarrays.

The array of elements E1 320 through En 325, in one embodiment, isconnected, in serial or parallel configuration, to a slave processingunit 335 (referred to herein as “SPU”). In one embodiment, the slaveprocessing unit 335 can comprise a Field Programmable Gate Array(“FPGA”), a Complex Programmable Logic Device (“CPLD”), amicrocontroller, etc. The slave processing unit may also be placedwithin the elements (internalized) labeled E1 to En thereby minimizingor altogether obviating the need for an external slave processing unit335. The elements in conjunction with the SPU form a “sensor module”330.

One or more sensor modules 330 may be placed in a configuration that isoptimized to maximize system performance. For example, multiple sensormodules 330 could be configured to operate in parallel so as to increasethe sensitivity of the device. Because each of the elements may only bemodestly sensitive in detecting incident particles, the overallsensitivity to the particles being detected can be increased by stackingmore than one sensor module 330 in parallel.

Each element may be only modestly sensitive in detecting incidentparticles, but when several of these elements are aggregated in anappropriate architecture, these components act in a concerted fashion toresult in a highly sensitive, agile and reliable particle detectioninstrument. The aggregation of sensors operating in parallel results inhigher sensitivity to the particles and resultant imaging as compared toindividual elements or an individual module. In one embodiment, themultiple sensor modules can be loaded onto and operate in parallel on acommon printed circuit board. In a further embodiment, multiple printedcircuit boards, each with at least one sensor module, can be configuredto operate and detect particles in parallel to further increase thesensitivity and fidelity of the platform. In one embodiment the multiplesensor modules can all be configured to detect neutrons making thedevice highly sensitized to neutrons and, accordingly, a highly reliableneutron detection instrument.

Each of the modules can be comprised of multiple elements. In oneembodiment, the elements, E1 320 to En 325, can be made as large orsmall as needed in order, for example, to embed them in confinedgeometries such as inside the human body for medical applications suchas single-photon emission computed tomography (“SPECT”), positronemission tomography (“PET”), etc.

In one embodiment, a subset of the elements E1 320 to En 325 can beconfigured to detect different particles from the remaining elements bycoating them with a different C1 converter layer from the otherelements. Accordingly, a single sensor module 330 can be used to detectmore than one type of subatomic particle.

Each sensor module 330 is connected to, either wirelessly or throughwires, to a system level master processing unit 345 (referred to hereinas “MPU”) that controls the operation of the SPU 335 on the module andprocesses the data it receives from the SPU 335. In one embodiment, theSPU 335 in one of the modules may also be able to serve as the MPU 345.An MPU 345, in one embodiment, may be connected to several sensormodules 330, wherein each sensor module 330 is configured to besensitive to and detect a different subatomic particle. Alternatively,an MPU 345 may be connected to several stacked sensor modules 330 actingin concert to detect the same particle, e.g., neutrons.

In one embodiment, the MPU 345 may be part of a computing system similarto computing system 110 from FIG. 1 described above in detail. Further,the MPU 345 may also comprise a system memory 116 and storage devices132 and 133 for storing data received from the various sensor modules330 similar to computing system 110 in FIG. 1. The MPU 345 may sendprocessed data to the display 350 that has a user interface (UI) thatcan be used to program the entire system. The display 350 may perform asimilar function to display device 124 discussed above in relation toFIG. 1.

Further, the data from the MPU 345 may also be relayed wirelesslythrough wireless module 380 to a host server 370, wherein the hostserver may perform a similar function to servers 240 and 245 describedin relation to FIG. 2. Each of the client devices 210, 220 and 230 inFIG. 2, in fact, may be a discrete computing system comprising a MPU,connected to its own set of SPUs, and reporting the results of aparticle detection operation to a host server 240 or 245 through network250. For example, client devices 210, 220 and 230 may be securitydevices installed at an airport to screen passengers' baggage forexplosive devices. Each of the client devices could then, in turn,report the results of the screening to a centrally located server 240 or245. The results from all the various screening operations could also bestored in storage devices 260(1)-(L), storage devices 270(1)-(N),storage devices 290(1)-(M), or intelligent storage array 295. In anotherembodiment, MPU 345 may relay data to host server 370 through a wiredconnection (not shown) instead of through wireless module 380.

In one embodiment, the data from the various SPUs could simply flowthrough an MPU and be transmitted to a host machine 360. The hostmachine, in one embodiment, could be a personal computer or a tablet PCor even a smart phone that may be a computing system similar tocomputing system 110 from FIG. 1 described above in detail. The hostmachine in such an embodiment would be connected to the MPU 345 througha communication interface similar to interface 122 described in detailabove.

In this embodiment, the host machine 360 would be responsible forprocessing the data received from the various SPUs instead of the MPU.The MPU would, however, be responsible for controlling the operation ofthe various SPUs connected to it. The host machine would thereforeperform a similar function to computing system 110. The display 350 maythen be connected to the host machine 360, wherein a user of the systemcould program the system using the display connected to the hostmachine. Alternatively, in one embodiment, the MPU 345 may reside onhost machine 360 instead of within the housing 340 of the particledetection system and control the various SPUs from within the hostmachine 360.

In one embodiment, the particle detection system of FIG. 3 isencapsulated for protection from the element such as temperature,humidity, dust, etc., by placing it inside a housing 340 made frommaterials such as plastic, metal, etc. The housing 340, in oneembodiment, may be designed to restrict the entry of certain subatomicparticles, such as photons in the visible range, ultraviolet range, ormore energetic photons such as X-rays or gamma rays, etc. For certainapplications, such as neutron detection, for example, the housing 340may contain materials such as high-density polyethylene (“HDPE”) thatmoderate the incident neutron velocity. The design of the housing 340and the materials used to construct it will vary depending on theapplication for the particle detection system. For example, if thedetector is being used for oil and gas exploration within oceanicwaters, the housing 340 will need to be constructed with materials ableto withstand extremely high subterraneous temperature and pressure.

FIG. 4 is a schematic block diagram illustrating a typical hardwareconfiguration for connecting the host machine with the sensor modules.The embodiment illustrated in FIG. 4 is one wherein the display 350 andUI are connected to or implemented within host machine 360 as describedabove. The host machine 360 is responsible for processing the data itreceives from MPU 345 over communicator bus 491. MPU 345 is responsiblefor controlling the operation of the various SPUs on sensor modules430A-430N. Sensor modules 430A-430N perform essentially the samefunction as sensor module 330 from FIG. 3. Each of the sensor modules430A-430N shown in FIG. 4 may be configured to detect a differentsubatomic particle. Alternatively, as discussed above, the sensormodules 430A-430N may be stacked and operating in parallel to reliablydetect the same particle, e.g., neutrons. By using a plurality of sensormodules acting in concert, the sensitivity and reliability of the systemcan be vastly improved.

The sensor modules 430A-430N constitute a module array that can plugdirectly into board sockets within the particle detection chassis 450.The MPU 345 may be populated on the same board that comprises thesockets for plugging in sensor modules 430A-430N, or one of the SPU's in430A to 430N may be programmed to serve the function of MPU 345, therebyeliminating the need for a separate MPU 345. Because they plug intoboard sockets, the sensor modules can be easily inserted and removedfrom apparatus 450. Further, the placement of the sensor modules430A-430N can be determined based on the type of particle each sensormodule is configured to detect and how sensitive to the particle theuser needs the system to be.

Host machine 360 uses communication interface 122, as illustrated inFIG. 1, to communicate with the particle detection apparatus 450encapsulated within housing 340 over communicator bus 491. Thecommunicator bus 491 provides a high-speed electronic communicationchannel between the host machine 360 and the particle detectionapparatus 450. The communicator bus can also be referred to as abackplane, a module connection enabler, or system bus. Physically,communicator bus 491 is a fast, high-bandwidth duplex connection busthat can be electrical, optical, etc.

Particle detection apparatus 450 can, in one embodiment, also be used ina standalone mode, such as a handheld instrument, backpack instrumentetc. In this embodiment, the housing of the apparatus 450 would compriseMPU 345, the display 350, a wireless module 380, and one or more sensormodules 330, so that the user could freely use the particle detectorwithout needing to physically connect to a host machine. The particledetection apparatus 450 can, in another embodiment, be also connectedthrough a wired (such as Ethernet or USB) or wireless (Bluetooth, Wi-Fi)to a computing device such as tablet PC or smart phone. In thisembodiment, there will be no need for a display 350 on the detectionapparatus. As discussed above, MPU 345 could be part of a computingsystem similar to computing system 110 illustrated in FIG. 1 with anassociated memory and display. Such a system, along with its modules,could serve as a component in an assembly of systems that would beplaced at desired locations arbitrarily far from each other to act asagents for detecting subatomic particles over large geographic regions,on land, underground, on water, underwater, or any other locationincluding space. Data gathered from the various agents may be relayed toa central host machine 370 and analyzed to prepare maps of incidentparticles across any geographic region.

In one embodiment, the module 330 can be programmed to determine therate of subatomic particles incident on it. Alternatively, the MPU canbe programmed to collect information from the SPUs connected to it anddetermine the rate of various subatomic particles incident on the entireapparatus 450. In another embodiment, particle detection apparatus 450can be configured to establish the direction of incident particles byplacing the modules 430A-430N within it in an appropriate geometricconfiguration, e.g., around a sphere, or in a stacked parallelconfiguration. For example, the direction of neutrons can be determinedby using a neutron absorbing collimator or neutron absorbing grid ofapertures in front of the detector apparatus that will block allneutrons incident on them and will only allow the passage of incidentneutrons (that align with and pass) through windows in the grid orcollimator.

In yet another embodiment, appropriate design of material used todevelop sensor pixel P1 315, such as fully depleted deep CMOS or CCDsensors made from inorganic or organic semiconductors, will allow thesystem to determine the energy of incident subatomic particles includingneutrons and thereby enable spectroscopy.

Further, in one embodiment, the entire system, or each module in thesystem, or even each element in the system can be tuned to be sensitiveto different subatomic particles. For example, module 430A can beconfigured to be more sensitive to gamma rays while module 430B can beconfigured to be more sensitive to neutrons. Conversely, the modules canalso be configured, in one embodiment, to be insensitive to certainsubatomic particles. One method to make the modules insensitive tocertain subatomic particles is to coat the converter layer C1 withappropriate blocking layers that reduce sensitivity to certainparticles. This chemical tenability is an advantageous feature of thepresent invention because it gives a user the unique flexibility toconfigure a system to be sensitive to a select subset of subatomicparticles of interest while being insensitive to other particles thatthe user may not be interested in tracking.

In one embodiment, choosing elements E1 320-En 325 that are highlypixelated can significantly increase the granularity of the particledetection device. For example, the more pixels an element can comprise,the easier it is for the system to detect the location and direction aparticular particle came from. It also makes it easier to detect theparticle's energy. FIG. 5A is an exemplary block diagram of a sensorarray of pixels in accordance with one embodiment of the presentinvention. As seen in FIG. 5A, the higher the number of pixels on pixelarray P1 315, the more granular it is and the easier it is to preciselydetect the position of particles 550.

FIG. 5B is an exemplary block diagram illustrating the cross-sectionalview for each pixel on the sensor array of pixels in accordance with oneembodiment of the present invention. As discussed above, the sensor maybe an off-the-shelf component typically found in a conventional digitalcamera. The electronics for the pixel may be mounted on siliconsubstrate, comprised of p-silicon 550 and n-silicon 560. The area of thepixel that collects information regarding incident particles, e.g.,photons is photo-diode 570. The pixel may also comprise threetransistors 580, T1, T2 and T3, that are used to collect the informationcaptured by the photodiode. For example, if subatomic particles orproducts of the reaction between the incident subatomic particle and theconverter layer (390 in FIG. 3) impinge upon photo-diode 570, voltage(or current) 595 is induced through a combination of distortion andionization of the electron field within the photodiode as well as thephotoelectric effect. The energy of individual incident subatomicparticles or products of the reaction between a single incidentsubatomic particle and the converter layer (390 in FIG. 3) impinging onphoto-diode 570 dictates how much charge accumulates within the pixels.If several particles become incident during the time when the sensor isin an exposed state, a proportionately larger number of islands ofpixels will accumulate charge. The transistors are used to collectinformation regarding the accumulated charge during a capture cycle andconvey this information to an A/D converter within control electronicsmodule 310. Each pixel may report an A/D converted value of between 0and 1024 based on the intensity of impingement on the pixel.

In certain embodiments, C1 390 may be reactive to more than one type ofsubatomic particle. For example, materials that react with neutrons mayalso react with high energy gamma rays. In another example, theconverter materials may interact only with neutrons but the sensormaterials may interact with a host of other sub-atomic particlesincluding gamma photons, alpha particles, fast electrons etc. In theseembodiments, a discrimination process may be run on MPU 345 that is usedto discriminate between the different types of particles whileminimizing any false positives. Each subatomic particle may be uniquewith respect to the intensity values they generate or the pattern inwhich they impinge on the pixels of pixel array P1 315. Thediscrimination procedure comprises information regarding all theparticles' unique “signatures” and uses these to differentiate betweenparticles to ensure that false positives are not generated.

For example, incident neutrons particles interact with the material inC1 or the material of the sensor pixels and produce one type of electricsignal and gamma rays produce another type of signal or pattern ofislands of pixels in which charge is generated beyond the thermallygenerated charges. Hence, discriminating between neutrons andnon-neutrons becomes much faster and simpler than in proportional tubesor scintillator detection systems that must collect a significant amountof statistical information in order to implement the pulse shapediscrimination algorithms for particle discrimination. The proposedsystem is capable of detecting single neutrons and being able todistinguish them from single non-neutron particles, such as gammaphotons.

This ability to discriminate between single neutron and non-neutronparticles is enabled by unique digital signatures for each type ofparticle. The term digital signature here refers to patterns of islandsof pixels where charge gets deposited by the incident particles orproducts of the interaction between the incident particles and theconverter layer C1. Therefore, not only can a neutron be distinguishedfrom other non-neutron particles, but also the non-neutron particles canbe further distinguished as gamma photons, x-ray photons, alphaparticles, fast electrons etc. Furthermore, every radioactive material(or radionuclide) emits a unique family of sub-atomic particles. Forexample, highly enriched uranium emits neutrons and gamma photons. Sinceat least some or all of these subatomic particles are detected anddiscriminated in the proposed system with the help of its discriminatingprocedure, the source (radionuclide/isotope, etc.) of these particlescan be identified by referring to a library of digital signatures orpatterns in the system's memory or a memory external to the system.

One application of the novel discrimination procedure of the presentinvention is in the oil and gas exploration industry. A drill used foroil exploration, for example, could comprise both a source of neutronsand the particle detection system of the present invention. Further, thedrill can comprise a source of gamma radiation as well. Both gamma andneutron data collected with the help of the novel discriminationprocedure provide vital information regarding the porosity and lithologyof rock formations.

Another application of the novel discrimination procedure would be inthe homeland security industry. For example, airport security scannersmay employ the particle detection system of the present invention todetect SNMs. However, because certain individuals carry radioactivity intheir body, they may radiate high energy gamma rays that would result ina false alarm being generated by the scanner if not for the particlediscrimination procedure of the present invention. As discussed above,certain materials chosen for C1 may react with both neutrons and highenergy gamma rays. Using the unique “digital” signature for theneutrons, gamma rays, and other particles, the discrimination procedureof the present invention prevents the generation of false positives. Theunique digital signatures also enable identification and counting ofgamma photons, as well as the identification of the source from whichthe neutrons and other particles originated. One example of thiscapability is that the discrimination procedure can distinguish betweena weapons grade Plutonium source and a non-neutron (and dominantly gammaemitting) source such as ¹³⁷Cs or ⁶⁰Co or ¹³³Ba.

FIGS. 6A and 6B illustrate two exemplary patterns created by twodifferent types of subatomic particles and as detected by a pixel arrayof sensors in accordance with one embodiment of the invention. FIG. 6Aillustrates a pattern created by hypothetical Particle A, while FIG. 6Billustrates a pattern created by hypothetical Particle B. If bothParticle A and Particle B are detected by the same sensor P1 315 becauseconverter layer C1 390 reacts with both types of particles, or theconverter layer C1 390 interacts with Particle A and the sensor materialinteracts with Particle B, then a discrimination procedure is requiredto be able to tell the particles apart so as not to generate falsepositives. The discrimination procedure will be programmed to recognizethat Particle A will create a pattern of islands of pixels of intensitystatistically different from Particle B, and further that the patternwill comprise of pixels that are clumped together as opposed to thediagonal or other types of pattern generated by Particle B. Accordingly,the discrimination procedure can use the respective signatures ofParticle A and Particle B to distinguish between each other.

The discrimination procedure can, in one embodiment, compare a patterncreated by a particle to a library patterns stored in memory 116 of hostmachine 360 to identify which of the patterns in memory the particlemost closely resembles in order to identify the particle.

It is important to note that in one embodiment of the present inventionthe signature patterns of various different particles can be identifiedat the same time. For example, the discrimination procedure would beconfigured to identify both Particle A and Particle B at the same timein the example illustrated in FIGS. 6A and 6B. Further, if otherparticles were detected in the system, those particles could beidentified using their digital signatures at the same time as well.

FIG. 9 illustrates exemplary signatures for neutron and gamma particlesused to distinguish between the two particles in accordance with oneembodiment of the present invention. The discrimination procedurediscussed above can be configured to detect pattern 910 associated withneutron generated alpha particles and distinguish pattern 910 frompattern 920 associated with gamma photons. Thus, discriminationprocedure can identify both neutrons and gamma photons and distinguishthem from each other.

In one embodiment, the digital signature can be generated using severalstacked sensor modules, e.g., 430A-430N in FIG. 4. In this embodiment,the generated digital signature can be a vector in three dimensionalspace. The discrimination procedure uses information, e.g., coordinatesof sensors, intensity of impingement based on the A/D read-out from thepixels, time of impingement etc. to determine a pattern of impingementin three dimensional space and compares the pattern to the digitalsignatures stored in memory and performs a statistical match in order todetermine the identity of the particle. Performing digital signaturecomparison in three dimensional space allows for increased reliabilityin the system. For example, certain particles with higher energy mayleave a pattern of higher intensity on the surface modules as opposed tomodules deeper within the stack. Or, for example, particles such asneutrons and gamma photons with higher momentum may leave a pattern ofhigher intensity on modules deeper within the stack but only a trail oflower intensity on the surface modules. Thus, analyzing the intensity ofthe reaction of the particles with the sensors at different layers ofthe module stack allows for increased fidelity and accuracy.

Furthermore, in one embodiment of the present invention the noisegenerated and accumulated within the pixels due to thermal or any otherreason, and especially during the time interval of exposure, can beeliminated. The methods for reducing or completely eliminating suchnoise may include: (a) timely resetting of individual pixels oraggregates of pixels or entire rows and columns of pixels within thepixels; (b) optimization of exposure, readout and reset time cycles sothat pixels are reset as often as is required; (c) changing thetemperature of operation of the sensors, such as cooling them down.

In one embodiment, the discrimination procedure may use patterns createdby charge building up in the pixels of the underlying sensor. Forexample, the neutrons may interact with the converter layer and undergoa nuclear reaction. For example, if the converter material contains¹⁵⁷Gadolinium, the reaction will be¹⁵⁷Gd+n=¹⁵⁸Gd*=>¹⁵⁸Gd+gamma+x-rays+IC e−+ACK e−. In one embodiment, theproducts of this reaction will enter the sensor and create a build up ofcharge in the pixel that they interact with first. The high energy ofthese reaction products will cause secondary ionization within the pixelthat will lead to enhanced charge build-up within the pixel.Furthermore, the high kinetic energy of these reaction products willalso cause them to scatter on to neighboring pixels and a track ofbuilt-up charges will be left within the sensor. The discriminationprocedure within the instrument examines these tracks and determines theform of the particle. Hence, if the discrimination procedure in theprocessing unit of the instrument (or module) determines that agamma-ray and/or an x-ray and/or an IC electron and/or an ACK electronwere found in the sensor, it will be concluded that a neutron interactedwith the converter layer and the neutron count tracked by the MPU isincremented by one.

Similarly, by way of another example, if the converter layer contains¹⁰Boron and the incident neutron interacts with ¹⁰Boron, the followingreaction will follow: ¹⁰B+n−>⁷Li+Alpha. These reaction products willtravel in nearly mutually opposite direction and one of them willinteract with the sensor, thereby leaving a unique build-up of charge.For instance, alpha particles have a very high rate of loss of energywithin semiconductors and solids in general. Consequently, the build-upof charge in pixels is found to be uniquely concentrated to a few pixelsonly. The discrimination procedure within the processing unit is able tointerpret the “signature” of alpha particles (or ⁷Li) uniquely anddiscriminate this signature against any other radiation that might beincident on the instrument, such as gamma rays. As a result, theinstrument is able to discriminate neutrons from any other sub-atomicparticle.

The present invention is highly scalable because not only does it userelatively cost effective off-the-shelf components that may bechemically tuned using appropriate converter layers or convertermaterials blended with sensor materials, but also users have the abilityto incorporate as many sensor modules within an apparatus as needed.Further, because the parts of the present invention are readilyavailable and low cost, they are relatively easy to replace.Accordingly, if a sensor module gets damaged, it will typically be lesstroublesome to replace it than to fix it.

Further, another advantage of the present invention is that the housing340 of the particle detection system is flexible and can be configuredin ways specifically customized for several different applications. Forexample, the housing may be chosen in a way so that the pixel arrays canbe stacked or tiled side by side along a wall of a cargo container, andused to detect radiation in containers being shipped. In particular, forexample, in the case of neutron detection, there is great flexibility inhow the pixel arrays are arranged because, with some very limitedexceptions, neutrons can penetrate most matter until they make contactwith a material that they interact with. Also, as discussed above,particle detection apparatus 450 can be configured to establish thedirection of incident particles by placing the modules 430A-430N withinit in an appropriate geometric configuration, such as around a sphere.In this case, the housing 340 would be spherical. Alternatively, inother embodiments, the system can be designed to fit in a hand helddevice or a backpack device.

In yet other embodiments, the modules 330 and any other printed circuitboards (“PCBs”) within the housing 340 may be constructed using flexiblematerials, so that the system can be imbedded in clothing and otherareas where using rigid materials would not be pragmatic. Further, usingflexible materials allows the surface area of the detector to increase,thereby, increasing the sensitivity of the system. This advantageouslyallows the present invention to be utilized for various differentapplications using the same system design.

In one embodiment, the MPU 345 processes the data from the various SPUsit is connected to and performs all the calculations necessary todetermine if a particular particle has been detected. The MPU 345 canuse the information from the pixel arrays of the elements E1 320 throughEn 325 to determine precisely the coordinates of the pixels that testedpositive for the particle. The MPU 345 may create a vector ofinformation for each pixel comprising the coordinate of the pixel andthe element and sensor module it is located within.

FIG. 7 illustrates an exemplary information vector created for eachpixel by the MPU in accordance with one embodiment of the presentinvention. The information vector 700 may comprise information indiscrete fields regarding the pixel number or (x,y) coordinate 710,information regarding the element or sensor number 720, and informationregarding module number 730 on which pixel 710 and element 720 reside.Also, the vector may comprise information regarding the intensity value740 read out from the pixel and a timestamp 750. This vector ofinformation can either be stored in memory for further analysis orpassed on to display 350 for a user to visually analyze the data orpassed along to a computing device (such as a tablet PC or smart phone)attached to the detector box through a wired or wireless connection.Alternatively, the information may be relayed to a remote locationthrough wireless module 380. The MPU 345 may also compare the vectorsreceived from a pixel array to the various signatures of differentsubatomic particles stored in memory to determine or confirm theidentity of the particle.

Further, the MPU 345 can be programmed to flag an alarm for the user ofthe system if more than a critical threshold number of particles aredetected over a certain period of time and over a certain area. Forexample, in one embodiment, when the detection instrument is rendered asa handheld instrument homeland security applications, the MPU 345 may beprogrammed to flag an alarm on the display 350 if more neutrons aredetected per unit volume of the instrument than the background.

FIG. 8 depicts a flowchart 800 of an exemplary computer controlledprocess of detecting subatomic particles, according to an embodiment ofthe present invention. The invention, however, is not limited to thedescription provided by flowchart 800. Rather, it will be apparent topersons skilled in the relevant art(s) from the teachings providedherein that other functional flows are within the scope and spirit ofthe present invention. Flowchart 800 will be described with continuedreference to exemplary embodiments described above, though the method isnot limited to those embodiments.

At step 802, neutrons, or other subatomic particles, may be lensedtowards a sensor E1 320 by using a lensing apparatus L1 305. Focusingthe neutrons towards the sensor improves the instrument's sensitivity asdiscussed above.

At step 804, a reaction is generated when the neutrons, or othersubatomic particles, come into contact with converter layer, C1 390. Theconverter layer can interact with the incident neutrons to generate areaction, the results of which are then converted by a sensor array ofpixels, P1 315, to a readable electrical signal at step 806 usingcontrol electronics module 310. As discussed above, in one embodiment,converter layer C1 may comprise multiple layers of materials thatinteract with different subatomic particles, including neutrons, or itmay be a composite of materials, each of which interact with a differentsubatomic particle. Further, in one embodiment, instead of being adiscrete layer, the C1 layer may be intermixed with the sensory array P1315 itself.

At step 808, SPU 335 processes the signal from the various elements, E1320 to En 325, to generate pixel data for each sensor. While eachelement E1 320 to En 325 individually may have modest sensitivity fordetecting the incident subatomic particles, the elements in aggregateresult in a highly sensitive level of detection.

At step 810, the pixel data is transmitted to MPU 345. The MPU 345controls the various SPUs connected to it, collects the data from theSPUs, and analyzes the data at step 812 to determine the impingement ofany neutrons on the pixels of sensor 315. At step 814, the MPU 345 runsthe discrimination procedure used to discriminate between the differenttypes of particles without generating any false positives. For example,the MPU 345 may be programmed to discriminate neutrons from otherparticles such as high energy gamma rays may be coincident with theneutrons.

Physical Structure for the Tunable Sensor

FIG. 10 illustrates the physical architecture of a sensor in accordancewith an embodiment of the present invention. FIG. 10 illustrates across-sectional view of a typical sensor module 330 (discussed inconnection with FIG. 3). The sensor illustrated in FIG. 10 can either bea CMOS sensor or any charge detection device, e.g., a pin diode.

As discussed previously, neutrons are particles with no detectableamount of charge. In order to detect them, a sensor must interact withthem in a manner that generates a detectable signal. For most neutrondetectors, this signal tends to be charge. Hence, neutron detectors arealmost always neutron-induced charged particle detectors.

Further, as mentioned above, historically, detectors for neutrons havebeen largely analog sensors comprising a gas filled tube with a neutronsensitive coating on its inner walls, or some type of scintillatormaterial that generates photons when neutrons interact with it. Thesephotons are then detected by an underlying sensor typically afterphoto-multiplication.

Embodiments of the present invention are considered solid state neutrondetectors that do not rely on scintillation principles and, therefore,operate differently. Embodiments of the present invention instead relyon converter-on-semiconductor technology. Converter-on-semiconductortechnology utilizes a neutron reactive layer that (1) absorbs theneutrons, (2) causes a nuclear reaction to produce ionizing reactionproducts where (3) the ionizing reaction products create paths ofionized electrons and holes through the semiconductor that (4) areextracted and measured by an applied voltage between an anode and acathode. Embodiments of the present invention are superior to prior artmethods of detecting neutrons because semiconductors are very conductiveand, therefore, the sensors can operate at a lower applied voltage,e.g., 5V-25V. Further, the neutron reactive material is denser, allowingfor more efficient capture of ionizing radiation. Hence, the detectorcan be made much thinner without sacrificing detection efficiency.

In embodiments of the present invention, for example, a neutronsensitive layer 1004 that serves as a trap for neutrons is placed inclose proximity to a charge sensitive device 1006. It should be notedthat while the discussion herein is focused towards neutrons, asexplained above, embodiments of the present invention can be used todetect other types of particles, e.g., by changing the convertermaterial. Neutrons interacting with this layer 1004 produce chargedparticles such as alpha particles and triton particles that, dependingon their energy, have a certain spatial range they can travel beforethey lose all their energy. The rate at which they lose energy in amedium, also called −dE/dx, is highly nonlinear in that these particleslose energy at a higher rate with respect to distance as they slow downand become less energetic.

As shown in FIG. 10, a typical sensor for detecting particles, e.g.,neutron, gamma, alpha, beta, etc. will comprise a thin film of neutronconverting layer 1004 and an underlying charge sensitive semiconductordevice 1014. The charge sensitive device 1014 which, as discussed above,typically comprises a semi-conductor based CMOS device (such as asilicon CMOS sensor, bulk hetero junction polymer diode, or organicsemi-conductor-based CMOS sensor), PIN diode, or a photovoltaic device.The pixels 1006 that detect charge on the charge sensitive device 1014are typically mounted on a silicon wafer substrate 1007. In other words,the charge detection layer 1006 comprises multiple sensing elements orpixels that are mounted on a substrate 1007. In one embodiment, thecharge detection layer is between 5 and 300 microns in width. Asdiscussed in connection with FIG. 3, each sensing elements E1 320 to En325 comprises a pixel. In one embodiment, there is an air gap 1005,e.g., 10-200 um, between the converter material 1004 and the chargedetection layer 1006 as seen in FIG. 10. In one embodiment, the gap 1005may also be a vacuum (and not necessarily filled with air).

In one embodiment, the neutron converting layer 1004 is a film that maybe coated on a substrate 1003 such as glass or plastic or silicon or anyother material that does not interact strongly with neutrons. Thesubstrate 1003 can also be carbon fiber, polyethylene, cadmium, highdensity polyethylene, certain types of metal like steel, aluminum,cadmium, etc. The purpose of the substrate can be to protect theunderlying sensor and to filter out certain types of particles. Eachtype of substrate has its own properties. For example, a cadmiumsubstrate will block out fast neutrons. A lead substrate will block outa significant amount of gamma particles. A plastic substrate may slowdown neutron particles and can be used for moderating neutrons. Thesubstrate may also be used for filtering and conditioning of particles.It should be noted that while the embodiment in FIG. 13 is optimized fordetecting thermal neutrons, embodiments of the present invention may beoptimized to detect any type of particle.

In one embodiment, if the surface of the substrate 1003 chosen is smoothenough, another substrate film can be layered on top of the substrate(not shown in FIG. 13) in order to filter or condition different typesof particles from the primary substrate.

In one embodiment, the converter layer coating may also be made directlyon the surface of the charge detection device as discussed previously.In other words, the coating may be directly applied to the chargedetection layer 1006 without the air gap 1005.

In one embodiment, thermal neutron sensitivity depends on the converterlayer thickness. Thick converter layers capture more neutrons thanthinner layers. Thicker layers can be subject to reaction product loss,however, if they are too thick. Ideal thickness can range from 3 to 7microns for orthogonal front irradiation of ¹⁰B and 25-35 microns fororthogonal front irradiation of ⁶Li. However, ¹⁰B films can be anywherebetween 1 to 10 microns while ⁶Li films can be anywhere between 10 to200 microns.

The converter material coating 1004 is typically in the form of a thinfilm. The coated substrate is placed on top of the charge detectiondevice with the coating facing the charge detection device (e.g., CMOSsensor, PIN diode, etc.). Typically, there is an air gap between the topsurface of the thin film coating 1004 and the bare surface of thecharge-sensitive device 1006. This air gap can be tuned to change thesensitivity of the neutron-detecting sensor. In a different embodiment,as indicated previously, the device can also have the neutron sensitivethin film 1004 deposited directly on top of the charge sensitive surfaceof the underlying semiconductor device.

It should be noted that the neutron convertor material 1004 togetherwith the charge-sensitive semiconductor device 1006 comprises theneutron sensitive component of the neutron sensor system of the presentinvention (hereinafter referred to as the “neutron sensing elements”).

The neutron sensing elements can be of various kinds. As explainedabove, the charge sensitive device 1014, for instance, can be a siliconCMOS sensor of the kind used in off-the-shelf digital cameras. TheseCMOS sensors are designed with several small pixels that serve asindividual detectors of charge within the sensing element. The chargedetection layer 1006 in FIG. 10 comprises the pixels.

Neutrons incident on the conversion layer 1004 produce charged particlesand other reaction products when they interact with the material of theconversion layer. These charged particles make their way into the chargesensitive CMOS device 1014 through the bulk of the conversion layer1004, any air gap 1005 if present, and finally through any layers ofother passivating coatings on top of the CMOS device. By size, theseparticles are much smaller than the pixel. Their charge interacts withthe electron cloud in the pixel resulting in the dislodging of electronsand creation of holes within the silicon lattice. These electrons andholes are the charge carriers that get detected within the pixel assignal. Because reaction products from the neutron-conversion layer havea certain energy when they are created, they lose their kinetic energygradually within the charge sensitive device resulting in a finitelength scale over which dislodged charge carriers are localized.

As mentioned previously, C1, the converter material 1004, which is alayer of reactive neutron-capturing materials can comprise ¹⁵⁷Gadolinium(also known as “native Gadolinium” which is a mixture of severalisotopes including ¹⁵⁷Gadolinium), ¹⁰Boron (also known as native Boron),⁶Lithium (also known as native Lithium), etc. These converter layers maybe in pure elemental form or in compound form or be a mixture ofelements and compounds of neutron absorbing isotopes of elements in anycombination. For example, the converter layer could be a compound of⁶Lithium, e.g., ⁶Li—X, where X stands for any halide or iodide, e.g.,Fluoride, Chloride, Carbonate, etc. Or the material could be a compoundof ¹⁰B, e.g., ¹⁰B—X, where X stands for carbide or boric acid.¹⁵⁷Gadolinium may be either in its natural state or oxidized.

The material can be of amorphous, semi-crystalline or crystalline form.They can be deposited in the form of a thin film using a variety ofmethods including chemical vapor deposition (CVD) and liquid state orsolution state or sol-gel processing. Multiple layers of coatings withdifferent materials being sensitive to different kinds of incidentparticles can be deposited using a combination of methods.

Neutrons, e.g., neutron 1018 interacting with the C1 layer 1004 producecharged particles such as alpha particles (a particles), e.g., particle1019 and triton (³H) particles, e.g., particle 1020 that, depending ontheir energy, have a certain spatial range they can travel before theylose all their energy.

The air gap 1005 serves as an attenuator for the charged particle. Inother words, as the charged particle moves through this air gap, itloses energy gradually. The charge sensitive portion of the chargedetection device (e.g., charge detection layer 1006) needs to be placedat a location relative to the converter layer 1004 so that the chargedparticle loses most of its energy inside of the charge sensitive layer1006. The gap distance d1 1015 is therefore set so as to obtain themaximum signal within the charge detection device. In one embodiment,the range for d1 can be between 10 to 200 microns. Further, the typicalrange for the charge detection layer is between 5 to 10 microns and thedepth of the silicon wafer substrate is approximately 300 microns.

In an exemplary embodiment, when a neutron 1018 enters the sensor, itfirst passes through the substrate 1003. Typically, the substrate willbe chosen so that it does not block the particle of interest, e.g.,neutrons in the present case. The substrate may be chosen so that itblocks other type of particles, e.g., gamma rays, but it will typicallybe transparent to the particle of interest. For example, a substratemade of sheet metal or lead would be adequate to block gamma particles.

The neutron particle 1018 interacts with the converter material 1004 tocreate a creation. For example, if the converter material is ⁶Lithium,the following exemplary reaction may take place:¹ ₀ n+ ⁶Li→⁴ ₂α+³H

In other words, the neutron 1018 interacts with the C1 layer 1004 toproduce charged particles such as alpha particle 1019 and triton (ortritium) particle (³H) 1020. It should be noted that the charge of theproduced particles allows the particles to be detected by the detectionlayer 1006. The charged particles will typically dissipate in opposingdirections in response to the reaction between the neutron and theconversion layer. In other words, the alpha particle may travel in theopposite direction to the triton particle after the reaction takesplace. Nevertheless, the charge detection layer 1006 will detect thecharge from at least one of the charged particles, which in turn allowsthe sensor to flag the presence of the neutron 1018. It should also benoted that in order for detection to take place, the conversion layer1004 will typically face the charge detection layer 1006, so that thebyproducts of the reaction can be detected at layer 1006 easily.

When charged particles, e.g., alpha, triton, etc. are created followinga reaction with the converter material 1004, they typically travelextremely fast initially. But they start to lose energy at anexponential rate. Typically, when traveling, the charged particles havea certain distance they are able to penetrate into the silicon substrate1007. For example, a charged particle may get 50 microns deep into thesilicon substrate 1007 before losing all of its energy if the convertermaterial was deposited directly onto the pixels. Accordingly, in orderto ensure that the charged particle deposits most of its energy in thecharge detection region 1006, which is the most sensitive part of thesensor, the air gap is added to the design of the sensor. The air gapensures that the charged particle loses some, but not all, of its energyprior to making contact with the charge detection layer 1006. If theconverter material 1004 is deposited directly onto the charge detectingdevice 1014, most of the charged particles would likely penetrate toodeeply into the substrate to be detected. In one embodiment, the gapdistance d1 1015 is optimized so that the charged particles deposit themaximum amount of energy in the charge detection region 1006.

High Spatial Resolution Debris Mapping Application

Embodiments of the present invention can be used to performidentification and high spatial resolution mapping of nuclear fueldebris in or around damaged nuclear reactors. For example, the core of adamaged nuclear reactor that overheats may melt and core debris may leakfrom the reactor's pressure vessels (RPVs) that contain the fuel rods.Subsequently, the core debris may relocate into the containment vesselsthat are stabilized by the flow of cooling water. It would be importantin such circumstances to make use of a flexible detector capable ofwithstanding extreme conditions that can be used to identify and createa map of the fuel debris within the containment vessels without beingdestroyed.

FIG. 11 is an exemplary diagram of a cross-section of a nuclear primarycontainment vessel (PCV) of a reactor that may contain nuclear sedimentresulting from a nuclear accident. The primary containment vessel 1112comprises a thick reinforced concrete floor 1140 at the bottom of thePCV. It further comprises a cooling water pool 1110 that may containsome of the nuclear debris.

Simply detecting gamma radiation within the PCV does not help identifyfuel debris or the location of the debris with any certainty becauseinside the damaged reactor units, nearly everything emits some amount ofgamma. Factors further complicating identification of fuel debris aremelted fuel mixing, unknown geometric constraints of the debris field,and background activation of non-fissile material. If a gamma detectorwithin the damaged reactor units records high levels at a certainlocation, there is no certainty this gamma is emanating from the debris.

Spontaneous fission neutrons emitted from the core debris, if accuratelydetected, however, can characterize debris distribution. The challengeis that the neutron flux is low compared to the high gamma backgroundfrom deposited fission products (e.g., radioactive Cesium-137). Thisrequires detecting a low proportion of neutrons in a potentially highgamma background environment. The high energy environment is also fatalto most sensor equipment.

Embodiments of the present invention advantageously provide a sensorsystem that combines the ability to discriminate low neutron flux underhigh gamma backgrounds and robust enough to survive and function withinan extreme energy environment. Embodiments of the present invention aresensitive to ionizing radiation via direct detection in thesemiconductor and to neutrons via a neutron converter layer, e.g., layer1004 in FIG. 10. Discrimination between gamma and neutron counts occurin 2D 60 frames per sec (fps) videos via pixel intensity and 2D shape,contextually performed by proprietary machine learning software.

Embodiments of the present invention can be used for several potentialapplications in the context of fuel debris detection and spatialmapping. For example, the sensor system of the present invention can beused to map core debris in the PCV, RPV and suppression chamber of anuclear reactor. During debris removal, the detector can be used to sortfissile from non-fissile material. Further, the detector can be used forre-criticality monitoring.

In one embodiment of the present invention, a self-propelled robotequipped with a detector (comprising multiple sensors) can be programmedto enter the PCV and take measurements of gamma dose and neutron flux atvarious points on the metal grating 1140 and below the metal grating1140 of the PCV 1112. The neutron sensor of the present invention isable to remain usable in at least a 1,000 Gy/hr environment and whilereceiving exposure up to a cumulative radiation dose of 1,000 Gy. Bycomparison the background radiation of gamma under ordinary conditionson Earth is 10⁻⁴ Gy. Using embodiments of the present invention, therobot is able to create a high spatial resolution debris map of the PCVand identify the location of potentially harmful radiation sources(using triangulating techniques discussed further below).

In one embodiment, the detector is installed into the self-propelledinvestigation robot using a cylindrical case. Further, the detectorconfiguration and cabling is advantageously customizable so thatdetector performance can be optimized in accordance with the sizerequirement for other applications such as criticality monitoring.Additionally, the neutron sensor is environmentally adaptable which isadvantageous for extreme environments such as nuclear reactors. Thesensor is able to perform accurate readings in a high humidityenvironment and under water. In one embodiment, the neutron sensor isalso remotely operable so that the sensors can be controlled even whenplaced in extreme environments.

In one embodiment, a detector with multiple sensors can include a camerafor imaging in the visible range using an LED system for illumination athigh resolution without sacrificing lifetime.

Triangulating a Source Location for Neutron Particles

Typically, there is a need in many applications to know whether aparticular location in space is emitting radiation (such as neutrons andgamma particles) due the presence of an object that might have materialsundergoing fission or some other nuclear process. For example, in thedebris mapping application described above, there is a critical need inthe case of a damaged nuclear reactor to identify all possible sourcesof radiation in order to determine if the areas need to be evacuated orcleaned up. The sensing elements, e.g., SPUs of the present inventioncan be configured geometrically in various different ways to scan anarea in order to locate for sources of radiation.

For example, the sensing elements or SPUs can be arranged around a cube,cuboid, sphere, icosahedron, etc. Each of these configurations isreminiscent of a compound “eye” that is scanning some or all directionslooking for neutrons and other subatomic particles.

FIG. 12 illustrates the manner in which the sensing elements can beconfigured in a stack formation in accordance with an embodiment of thepresent invention. In one embodiment, the sensing elements 1205 placedinto an array can also be stacked in a stack formation 1210 to improvethe detection efficiency. One or more sensor modules may be placed in aconfiguration that is optimized to maximize system performance. Forexample, multiple sensor modules 1205 could be configured to operate inparallel so as to increase the sensitivity of the device. Because eachof the elements may only be modestly sensitive in detecting incidentparticles, the overall sensitivity to the particles can be increased bystacking more than one sensor module 1205 in parallel. FIG. 12 alsoillustrates a side view 1220 of a sensor array and a top view 1230 ofthe stacked sensor modules looking down on the stacked arrays.

In one exemplary embodiment, each of the sensor arrays 1260 can have asmall form factor and can be designed to pack in a large number ofsensors. For example, in the arrays shown in FIG. 12, each sensor can be1 cm×1 cm in size. Each sensor array can be 4 cm in height and 6 cm inwidth. Furthermore, the arrays can be stacked 6 cm deep in order to forma stack of arrays that is designed for a 10 cm×10 cm scan area. Itshould be noted, however, that the sensors, the arrays or the stack arenot limited to any particular size and can be of any size or shape. Thestacked sensor array embodiment is most sensitive to particles thatoriginate from a source that faces the sensor arrays.

FIG. 13 illustrates the manner in which the sensing elements can beconfigured in a cubical formation in accordance with an embodiment ofthe present invention. As mentioned above, the chips can be arrangedinto an array on a board. These boards can be of any size. Each chipwithin the board works independently. As shown in FIG. 13, the sensorarrays can be arranged in a cubical form with CMOS chips on sensorboards along each face of the cube. This allow multi-directionalsensitivity. Further, the sensor arrays on each side of the cube can bestacked so as to include multiple arrays parallel to each other in thecube orientation to increase sensitivity. Neutrons incident on each faceare detected on that face alone. The sensor chips form the buildingblock for generating high resolution 3D maps of debris that can, forexample, be used in the application with damaged nuclear reactorsdiscussed above.

In one embodiment, the configuration, such as the one shown in FIG. 13,also include a neutron blocking material that would reduce “cross talk”amongst the sensors within the configuration. For instance, a neutronblocking material 1310 such as natural or ¹⁰B enriched Boron in itselemental form or in compound form such as carbide, nitride or embeddedin plastic (such as HDPE) would block neutrons. In different embodimentswith different particles of interest besides neutrons, the blockingmaterial may be chosen accordingly to block such particles.

As shown in FIG. 13, the sensor boards 1320 are easily arranged around acube of neutron blocking material 1310. Neutron blocking material 1310is opaque to neutrons. With this configuration, neutrons incident on oneface of the cube are detected on that face alone. This is because inplanar sensing elements, the detection sensitivity varies significantlywith angle relative to the radiation source. It is maximum when thesensing elements are face-to-face with the radiation source. When thesensing elements are at 90 degrees to the source, the sensitivity issignificantly lower. For example, as shown in FIG. 13, the side 1370 ofthe cube facing the neutron source 1350 will be the most sensitive tothe neutrons and will likely detect the most number of neutrons.

Neutrons that do not get detected on one face make their way into theneutron blocking material 1310 where they get absorbed. In other words,these “undetected” neutrons never make it to the boards arranged on anyof the other faces of the cube. This reduces cross-talk between thedifferent sensor arrays and ensures that a clear determination can bemade with respect to the direction of the radiation source. Further, itallows a cleaner mapping of the particles that can be created fordisplay to a user.

As mentioned above, neutrons incident on one face of the cube willlargely be detected by the sensing elements on that face. Since thesensing elements can be pixelated, with each pixel serving as adetection element, the angle between a sensing element and the sourcealso creates a gradient of detection within the pixels. Within the samesensing element (e.g., the same SPU), the pixels closest to the sourcewill likely detect more neutrons than the pixels farther away. By usingthe counts and profiles of neutrons detected on each side of the cube,it becomes possible to determine the location of the source bytriangulating the results from all the elements on different sides ofthe cube. As shown in FIG. 13, when the information from the multiplesensors is processed, a 3D spatial map of the neutrons would typicallyindicate that based on the map, the most neutrons were detected on side1370 of the cubical sensor. Accordingly, it can be deduced that theneutron source 1350 is directly across from side 1370 of the cube.Furthermore, the analysis can be conducted down to the sensor level. Forexample, certain sensors on side 1370 may detect more neutrons thanother sensors on side 1370. Using the readings from each of the sensorson each side of the cube independently allows an engineer analyzing thedata from the sensors to further triangulate the source of theradiation.

The neutron blocker 1310 in the core of this cube serves to blockneutrons incident on one side of the cube from reaching the other sidesof the cube. This allows for better angular and spatial resolution.

In some embodiments, neutron directional information may also beobtained from the neutron itself, e.g., where the neutron is a fastneutron such as fast neutron 1345. In other words, the type of neutrondetected may also provide some directionality information.

Certain radioactive sources, e.g., Plutonium-239 emit an entire spectrumof neutrons. For instance, Plutonium-239 can emit slow neutrons, thermalneutrons, moderate neutrons and fast neutrons.

Thermal neutrons 1340 or hot neutrons are typically slow moving, e.g.,traveling at less than 2.5 km/s. Thermal neutrons travel slowly incloud-like formations with other thermal neutrons, do not contain anydirectional information, and are typically detected by the convertermaterial. To detect thermal neutrons, the counts of thermal neutronsdetected on each side of the cube (based on the reaction with theconverter material on each sensor) are used and the location of theneutron source is determined by triangulating the results from all thesensing elements on different sides of the cube as discussed above.

However, detecting fast neutrons that travel at speeds over 10,000 km/sis more complex. Fast neutrons provide an added advantage over thermalneutrons in that because of their speed, they are also carryingdirectional information. This directional information, for instance, canbe used to determine the source of the neutrons. However, fast neutronstypically cannot be detected by the converter material, which makestheir detection more challenging.

In one embodiment, the fast neutrons, e.g., fast neutron 1345 caninteract directly with the CMOS sensor itself rather than the coating1004. In other words, the fast neutrons interact directly with thecharge detection layer 1006. Interacting directly with the siliconmaking up the charge detection layer, the fast neutrons leave asignature behind on the silicon that allows a discrimination procedureto determine the direction of the fast neutron. The fast neutron willtypically interact with the silicon by destroying one or more siliconatoms it comes in contact with. Further, the fast neutrons upon contactwith the silicon release highly charged particles that leave a trail ofcharge behind on the silicon from which the directional information canbe deduced. The directional information can then be used to ascertainthe source of the neutrons.

In one embodiment, in order to detect fast neutrons the same way asthermal neutrons, the substrate 1003 can be designed with plastic orsimilar material. Plastic contains hydrogen, which slows down a fastneutron and turns it into a thermal neutron. Thereafter, the convertermaterial 1004 can detect the fast neutron, which has lost all its energyafter passing through the plastic substrate, in the same way as athermal neutron.

In one embodiment, if there is a high proportion of fast neutrons in theenvironment, the converter material 1004 for one or more sensors in thedetector can be designed with Cadmium. Cadmium has the rare property ofbeing able to interact with fast neutrons.

FIG. 14 illustrates the manner in which a sensor cubes can be made tofit within a cylindrical sensor head in accordance with an embodiment ofthe present invention. As noted above, in order to install a detectorinto the self-propelled investigation robot to be used for surveying adamaged nuclear reactor, a cylindrical case can be used. It should benoted that the sensor head may be a different shape other thancylindrical as well. The cylindrical case 1410 can house at least onecubical sensor 1430 in this embodiment. As shown in FIG. 14, the sensorsare arranged in a cube with a neutron blocker 1440 in the middle of thecube which allows each face of the cube to be directionally sensitive.

FIG. 15 illustrates the manner in which multiple sensor cubes can beconfigured to fit within a cylindrical sensor head in accordance with anembodiment of the present invention. The CMOS chip cubes 1520 form thebasis for 3D mapping of debris. Further, a stacked cube 1530, can alsobe used for added sensitivity. In one embodiment, measurements can bemade of the neutron counts on each chip of each board on each face ofthe cube. Because the cylindrical sensor head 1510 allows for both rolland pitch motion, the detector can effectively detect a volume of spacemultiple times since the cylindrical sensor head can rotate theequivalent of 360 degrees. Each time the sensor head is moved, the cubes1520 inside will get oriented slightly differently providing newinformation in the form of point cloud data. Subsequently, the measuredneutrons are reconstructed using techniques similar to tomography togenerate an accurate and high-resolution 3D map of debris.

In a typical embodiment, a cubical sensor configuration allows debrisscan of 10 cm×10 cm area effectively to generate a map with low spatialuncertainty. Spatial resolution of better than 10 cm can be achieved incircumstances where the detector can get close to the debris. In oneembodiment, by making the cubes 1520 smaller than 10 cm, 3D maps ofspatial resolution less than 10 cm can also be generated.

The CMOS chips used in embodiments of the present invention offerenormous flexibility in design and range of options for configuration.For instance, the boards can easily be stacked to improve sensitivityalong each face of the cube. Stacked sensor 1530, for example,illustrates a cubical sensor where multiple boards have been stackedalong each face of the cube to improve sensitivity.

In one embodiment, collimation can be included along with a stacked setof boards or with a cubical configuration to further improve directionalaccuracy. The chips can even be arranged around a sphere to create a“compound eye.”

FIG. 16A illustrates the various configurations that sensors can beplaced in to maximize sensitivity in accordance with embodiments of thepresent invention. For example, a collimated configuration 1610 can beused. Alternatively, the sensor arrays can be configured in a cubeshaped configuration 1650. In one embodiment, as discussed above, toincrease sensitivity, the sensor arrays can be stacked on each face ofthe cube to create a stacked cube configuration 1670.

FIG. 16B illustrates a collimated configuration that can be used toimprove directional accuracy in accordance with an embodiment of thepresent invention. As shown in FIG. 16B a collimator 1690 can be usedaround a stacked set of sensory arrays 1680 to improve directionalaccuracy.

FIG. 17 illustrates a detector configured in the shape of a cube and howthat is used to generate a debris map in accordance with an embodimentof the present invention. The cubed sensor 1710 comprises a scalabledesign that can be used to generate a debris map with low spatialuncertainty. In one embodiment, the design can be used to perform adebris scan of an area of 10 cm×10 cm effectively, but a spatialresolution better than 10 cm can be achieved where the detector can getclose to the debris.

FIG. 18 illustrates the manner in which multiple cubed sensors can beused to enable more efficient debris mapping in accordance with anembodiment of the present invention. In one embodiment, several cubedsensors 1810 can be used to perform rapid scanning of the area underinvestigation. The ability to use multiple detector modules to performrapid scanning advantageously enables flexibility of operation andfaster results.

FIG. 19A illustrates a cylindrical configuration that enables multiplesensors to be stacked to increase sensitivity in accordance with anembodiment of the present invention. As discussed above, in a nuclearreactor, for example, the robot unit may be equipped with a cylindricalcase that has the ability to hold multiple sensor modules 1940 whichenables rapid scanning and also provides enhanced sensitivity toparticles of interest. Further, the cylindrical configuration wouldleave space 1980 in the cylinder for cabling and connections.

FIG. 19B illustrates another type of cylindrical configuration thatenables multiple sensors to be stacked to increase sensitivity inaccordance with an embodiment of the present invention. In theconfiguration of FIG. 19B, the sensors are arranged around a cylinder(in other words, the sensors are not only limited to being in a cubicalor stacked configuration). The sensor shown in FIG. 19B comprises an endcap 1956 to enable the sensor to be used in different modes, e.g.,side-facing, end-facing, etc.

FIG. 19C illustrates a type of cylindrical configuration that uses aneutron block to increase directional sensitivity in accordance with anembodiment of the present invention. The neutron blocker 1955 allows thetool to be directionally sensitive and turns the detector into a“compound eye” for neutrons. In one embodiment, the sensors could bestacked around the cylinder with more layers of sensors. For instance,the sensor shown in FIG. 19C may, in one embodiment, have sensors onboth sides of the printed circuit board, one side facing outward, whilewhile the other side is facing the neutron blocking core 1955.

FIG. 20 illustrates the manner in which CMOS device sensors and PINdiode sensors can be combined in the same detector system in accordancewith embodiments of the present invention.

As noted above, the charge sensitive device 1014 typically comprises asemi-conductor based CMOS device (such as a silicon CMOS sensor, ororganic semi-conductor-based CMOS sensor), PIN diode, or a photovoltaicdevice.

In one embodiment where multiple sensors are used to increasesensitivity, both Silicon CMOS sensors 2030 and Silicon PIN diodesensors 2040 may be used in parallel. The sensors are packaged together,connected by cable (of customizable length) to a power supply and dataprocessing systems. Enhanced sensitivity and accuracy of neutrondetection is achieved by cross-validation amongst sensors of differenttypes.

Embodiments of the present invention use multiple sensors, multiple PINdiodes 2040 and multiple CMOS sensors 2030 because sensor redundancyboosts survivability and reliability and sensor cross-validation boostsmeasurement accuracy. Embodiments of the present invention combinesilicon-based CMOS and PIN diode technologies. The CMOS sensor is ahighly accurate digital neutron and gamma detector. Modular CMOScomponents can be easily integrated into small- to large-sized detectorsbased on requirements. CMOS sensors are less radiation tolerant than PINdue to smaller feature size, and have a lower active area due topackage-to-volume ratio.

The silicon PIN diode, on the other hand, is more radiation tolerantthan CMOS due to larger fabricated feature size. The PIN diode has hightiming resolution and a higher active area package-to-volume ratio.Further, the PIN diodes lead to increased neutron counting efficiencybecause of the large sensitive area of a PIN chip especially underlimited space availability. Its modular components can be easilyintegrated into small to large sized detectors based on applicationsrequirements. However, PIN diodes tend to be less accurate than CMOS inneutron/gamma discrimination at low neutron/high gamma dose rate (andenergy) background, as they utilize only pulse discrimination and notpulse discrimination combined with 2D spatial recognition (as will bediscussed below).

When both CMOS sensors and PIN diode sensors are used together, thecombined sensor attributes overcome the disadvantages of each individualtype of sensor, and achieve fast and accurate detection in a range ofneutron detection environments. Further building redundancies into thesystem using multiple CMOS and PIN diode sensors is critical forapplications such as debris mapping because the adverse environmentalconditions are almost certain to cause a certain degree of sensorfailure. Therefore, some debris mapping applications may require as manyas 256 redundant sensors. Moreover, in conditions where the signal isreally faint, all the sensors can be turned on in order to boostsensitivity.

Transmission of Information from Sensors to the Command and ControlEquipment

In adverse environments, especially where extremes of temperature,moisture and radiation are present individually or in some combination,it is often desirable to minimize or completely eliminate components ofthe sensor unit that might get damaged. The extent to which a componentis likely to get damaged is typically proportional to the density oftransistors within the device. For instance, FPGAs, CPUs and GPUs aremore likely to get damaged in such environments than CMOS image sensorsor commonly available CMOS based data serializers. In other words, ifthe sensor being used is from a common off-the-shelf camera, theadditional electronics in the camera are much more likely to get damagedthan the sensor itself. Accordingly, it is beneficial, if embodiments ofthe present invention are being used in adverse environments, toseparate the sensor from the additional electronics using robust cablingor other methods in order to increase the lifetime of the sensor.

In one embodiment, in order to address the problem of the electroniccomponents being more sensitive than the sensor, a long distance may beput between the sensitive and the relatively hardier components usingcabling. The long distance potentially may help to get out of highradiation environments, for example. In this manner, the sensitivecomponents can be placed at a location where the environment is lessadverse resulting in higher system reliability and longer lifetime.However, spatial separation of such components also requires that datafrom the sensors now be transmitted over the distance of the separation.Also, spatial considerations often demand that data be moved over cablesthat are relatively thin and easy to manage. For example, in FIG. 19,since multiple sensor modules 1940 are stacked together and each sensormay potentially require its own cable, the cables need to be thin andflexible enough to fit into space 1980.

In one embodiment, each of the sensors (e.g. SPU) on an array maytransmit their results to an MPU (e.g. MPU 345 in FIG. 3), whichcoordinates the results from the various sensors and transmits them overa single cable. The MPU, in one embodiment, can be co-located with thesensors. As discussed above, the MPU can simply be another sensor or SPUthat is programmed to control the various other sensors and collect datacoming from the sensors. Also, co-located with the sensors is a dataserializer (e.g. serializer 2133) that can withstand extremeenvironments. The data serializer 2133 can serialize the informationfrom the sensors (or MPU) and send them over long distances to thecommand and control modules as will be discussed in relation to FIG. 21Abelow.

FIG. 21A is a logical diagram that illustrates the manner in which datais transmitted from the sensors to the command and control equipment inaccordance with an embodiment of the present invention.

In one embodiment, where the command and control modules need to belocated at a safe and potentially long distance from the sensors, datagenerated at the sensors 2110A-2110D can be serialized using dataserializers 2133 that operate in MHz to GHz frequencies. Serializing thedata allows the thickness of the cables to be minimized. The data cablesneed to be thin because there may be potentially several hundred sensorsplaced in the detection area and the cables needed to transport the datawill usually need to be designed to be able to fit into tight andconfining geometries. Data can be communicated over the cables using,for example, low-voltage differential signaling (LVDS). Options for datatransmission over thin cables 2198 include twisted pair cables, coaxialcables, optical fibers etc. In one embodiment, if the geometry is suchthat extremely thin data cables are required, and data must be movedover long distances in excess of 100 meters, data can be formatted intoInternet Protocol and moved. This way, data can be communicatedworldwide and directly moved to the cloud for processing. In a differentembodiment, data communication can take place over WiFi or Sonarchannels and the cables can be eliminated completely.

As noted above, the SPU 335 or MPU 345 (not shown in FIG. 21, butco-located with the sensor modules 2110A-2110D) can comprise FPGAs orother logic devices. The FPGA in an MPU, in one embodiment, canmultiplex the signals from the various SPUs and attach a timestamp andID number to the sensor information. The command and control module canthen use the timestamp and ID information to determine which of thesensors is communicating (in the case where a single command and controlmodule controls a number of sensors). In a different embodiment, each ofthe SPUs can also attach a timestamp and ID number to the informationfrom the various sensing elements and transmit the information to therespective command and control module.

In one embodiment, in instances where the command and control module canbe co-located with the sensors (e.g., in less extreme environments) andthe data does not need to be serialized, information from each of thesensors can be packetized by the SPU or MPU and header information canbe attached to each of the packets comprising sensor information, date,time, etc. before transmitting to the command and control module.

In a different embodiment, the sensor information can be directlycommunicated over transmission lines, where each sensor may communicateat a different frequency. The information is then synthesized andaggregated by the command and control modules. In yet anotherembodiment, each of the sensors is connected to its own respectivecommand and control module using its own associated cable, whichobviates the need to aggregate or multiplex the information.

In one embodiment, special protective shielding material is required forthe cables especially in cases where long distances need to be coveredthrough adverse environments. Effects of adverse environment includedegradation of cable material and noise induced by environmental factorssuch as temperature and radiation. For example, lead or tungstenshielding can protect the cable from intense gamma ray environments, andalso result in reduced noise from fast electrons produced by such gammarays being incident on the conductor part of the cable. In high neutronenvironments, the cable must be protected from neutrons by usingmaterials such as Boron, Gadolinium and Lithium.

In one embodiment, because of space constraints the thickness of each ofthe cables cannot be more than 3 mm in diameter. In one embodiment, bothoptical fibers and copper wire cables can be used to meet the systemrequirements effectively.

Both the medium used to transmit the signal and the protocol used tocommunicate need to maximize transmission speeds. This is because theinformation transmitted by the sensors needs to collected and analyzedin real time.

It should be noted that while FIG. 21A is illustrated with four sensormodules and associated command and control modules 2120A-2120D, there isno limit to the number of sensors or command modules that can operate inparallel.

Each sensor module 2110A-2110D, in one embodiment, is coupled to arespective command and control module 2120A-2120D. The command andcontrol module controls the sensor. In one embodiment, there can be asingle command and control module that controls all the sensors. In adifferent embodiment, each command and control module can control asubset of the sensors. In the embodiment shown in FIG. 13, each commandand control module is associated with a respective sensor. Having adiscrete command and control module builds redundancies into the systemso that if one or more of the command and control modules fail, thedetector does not go completely off-line. Each command and controlmodule comprises firmware that can be programmed to control the sensors,e.g., control when to turn sensors on or off, to command the sensors totransmit their data or stop transmitting data, etc. Further, the commandand control modules can comprise registers to store the data incomingfrom the sensors.

In one embodiment, the command and control modules can be incorporatedwith the sensors, where the adversity of the environment is not afactor. In other words, in circumstances where the sensors do not needto be separated out from the sensitive electronics controlling thesensors, the command and control modules can be co-located with thesensors.

As noted above in connection with FIG. 3, in one embodiment, the datafrom the various sensors (or SPUs) could simply flow through an MPU. TheMPU can be programmed to control a group of SPUs or sensors. In oneembodiment, an MPU (not shown in FIG. 21) that controls the sensors(2110A-2110D) could collect the information from the sensors andtransmit it to the associated command and control modules. The commandand control module may be entirely separate from the MPU, e.g., acomputer or server stack connected to the MPU. Alternatively, it couldhave some components in the MPU (e.g., an FPGA, Microprocessor, etc.)and some components outside the MPU (e.g. a computer or server stack).In a different embodiment, the SPUs or sensors can be programmed tocommunicate directly with the respective command and control moduleswithout the use of an MPU.

In the nuclear reactor example discussed above, the sensors 2110A-2110Dcould reside in the cylindrical casing fitted into the robot sent intothe PCV, while the command and control modules could be situated at asafe distance from the reactor. The distances, therefore, that thecables have to communicate would typically be around or above 60 meters.The sensors would be connected to the command and control modules usingthin cablings, e.g., twisted pair cables, coaxial cables, opticalfibers, etc. The thin cabling is important because if several sensorsare used, then several cables need to pass through confined and narrowgeometries.

In one embodiment, each of the command and control modules is programmedwith sensor information including the type of particle the sensor isoptimized to detect. For example, sensor 2110A may be optimized todetect neutrons while sensor 2110B may be optimized for the detection ofalpha particles.

As stated above, certain utility related applications require sensing ofalpha, neutron, gamma and beta particles at the same time. Sensors2110A-2110D may all be placed in a single detector with each sensorinside the detector optimized to detect a different type of particle.The associated command and control modules would be programmed withinformation regarding the type of particle the respective sensor moduleis designed to detect.

If one of the sensors is partitioned with different types of coatingsacross the sensor, then the corresponding command and control modulewould have to be programmed with that information as well.

In one embodiment, each command and control module is connected to arespective computer (e.g. computer 2130A-2130D). Alternatively, command,control and compute can all be part of the same computing module. Inother words, each computer (e.g., computers 2130A-2130D) can beintegrated with a respective command and control module. Furthermore,there can be a single computer rack dedicated to each sensor with therespective command, control and compute modules mounted on the rack. Inone embodiment, all the compute modules 2130A-2130D can be part of asingle master computer that controls all the connected command andcontrol modules.

In one embodiment, the command and control modules are programmed toreceive the serialized data over twisted pair or coaxial cables from theCMOS sensors in the detector system and convert the serialized data intoa sequence of bitmap images at the rate of 60 frames/second. In otherwords, the command and control modules perform the data formatting forthe information received from the CMOS sensors and convert them intobitmap images. It should be noted that while the discussion hereinfocuses on bitmap images, the command and control module may convert thesensor data into images of any format, e.g., jpeg, raw, png, etc.

These bitmap images can thereafter be analyzed by a discriminationprocedure executing on the compute modules 2130A-2130D to detect thepresence of various types of particles, e.g., gamma, neutrons, etc. Inone embodiment, computer modules 2130A-2130D executes particlediscrimination procedures that can analyze the bitmap images to detectthe presence of particles.

The signals from the command, and control modules are analyzed inreal-time by the compute modules 2130A-2130D and sent out to display2150 (which is similar to display 350 discussed in connection with FIG.3). The information collected from the CMOS sensors is displayed in 2D60 fps videos and proprietary machine learning software running oncompute modules 2130A-2130D is used to discriminate between gamma andneutron counts.

For example, artificial intelligence algorithms discriminate neutronparticle counts in a gamma particle background. Data processing isconducted in real-time and accurate discrimination in a high radiationenvironment is achieved through artificial intelligence (AI) algorithmsfor accurate discrimination of neutron and gamma particles at a softwarelevel (as will be discussed further below).

In one embodiment, the command, control and compute modules are allprogrammed in firmware on GPUs. Each sensor is typically transmitting 1MB of information per second and approximately 2 TB of data in 8 hours.Accordingly, high-speed GPUs are needed to manage the high volume ofdata being transmitted. As noted above, the data analysis and datastorage electronics associated with the command, control and computemodules are located farther away from the sensors to ensure reliabilityof the circuits and to protect them from adverse conditions.

FIG. 21B is a logical diagram that illustrates the manner in which datais transmitted from a robot in a nuclear primary containment vessel(PCV) of a reactor to a safe room with the command and control equipmentin accordance with an embodiment of the present invention.

As noted above, embodiments of the present invention advantageouslyprovide a sensor system that combines the ability to discriminate lowneutron flux under high gamma backgrounds. Embodiments of the presentinvention can be used for several potential applications in the contextof fuel debris detection. For example, the sensor system of the presentinvention can be used to map core debris in the PCV (e.g., containmentvessel 2184 shown in FIG. 21B), RPV and suppression chamber of a nuclearreactor. During debris removal, the detector can be used to sort fissilefrom non-fissile material. Further, the detector can be used forre-criticality monitoring.

As shown in FIG. 21B, in one embodiment of the present invention, aself-propelled robot 2181 equipped with a detector (comprising multiplesensors) can be programmed to enter the PCV 2184 and take measurementsof gamma dose and neutron flux at various points on the metal grating2182 (similar to metal grating 1140 shown in FIG. 11) and below themetal grating 2182 of the PCV. The neutron sensor of the presentinvention is able to remain usable in at least a 1,000 Gy/hr environmentand while receiving exposure up to a cumulative radiation dose of 1,000Gy. Using embodiments of the present invention, the robot is able tocreate a high spatial resolution debris map of the PCV 2184 and identifythe location of potentially harmful radiation source, e.g., nucleardebris 2183.

In one embodiment, the detector is installed into the self-propelledinvestigation robot 2184 using a cylindrical case. In one embodiment,the neutron sensor is also remotely operable, as in the case of robot2181, so that the sensors can be controlled even when placed in extremeenvironments, e.g., containment vessel 2184.

FIG. 21B provides an example of a scenario where the command and controlmodules need to be located at a distance from the sensors. Datagenerated at the sensor or sensor (equipping into robot 2181) can beserialized using data serializers that operate in MHz to GHzfrequencies. Serializing the data allows the thickness of the cables tobe minimized and for several cables to fit into confined geometries (inthe event that robot 2181 is equipped with several sensors). As notedabove, options for data transmission include twisted pair cables,coaxial cables, optical fibers etc. In one embodiment, if the geometryis such that extremely thin data cables are required, and data must bemoved over long distances in excess of 100 meters, data can be formattedinto Internet Protocol and moved.

In the scenario illustrated in FIG. 21B, the sensor and data extractionlogic are separated out from the sensitive electronics (as alsodiscussed in connection with FIG. 21A). Accordingly, the command andcontrol module 2192, the compute module 2193 and the display 2194 areall located at a safe room 2185 at a considerable distance from thecontainment vessel. Safe room may be several hundred meters away fromthe containment vessel and be safe for human entry. It should be notedthat while FIG. 21B is illustrated with a single sensor module, a singlecommand and control module 2193 and a single compute module 2193, thereis no limit to the number of sensors, or command, control and computemodules that can operate in parallel.

FIG. 22 illustrates the manner in which the sensor for the detector isseparated from the additional electronics in accordance with anembodiment of the present invention. As shown in FIG. 22, the CMOSsensors 2210, PIN sensors 2220, the camera 2230, and LED lighting 2240modules will typically be more durable and capable of withstandingextreme environments. Accordingly, they can be separated out from theprocessor 2260, the computer 2270 and the power supply 2280. In oneembodiment, the data aggregation module, e.g., the MPU 2250 can beco-located with the CMOS and PIN sensors. For example, in an instance,where one of the SPUs is programmed to be the MPU or data aggregatormodule, it can be co-located with the other sensor modules. In oneembodiment, the data aggregation module 2250 may also be a dataserializer that serializes the data to be transmitted to the command andcontrol modules. As noted above, it is beneficial, if embodiments of thepresent invention are being used in adverse environments, to separatethe sensors and camera from the additional electronics using robustcabling or other methods.

Use of Multiple Sensors to Ensure Reliability

In one embodiment, each of the sensors can be independently controlledusing the command and control modules (and associated MPUs). Individualcontrol over each of the sensors allows significant flexibility in thedetection system. When the signal is faint, all the sensors can beturned on. Alternatively, if the detector is in proximity to fueldebris, for example, and the signal levels are high, then all but onesensor can be turned off. In one embodiment, the command and controlmodule may be programmed with the logic to determine the number ofsensors that can be turned off in a high signal environment withoutsacrificing the integrity of the detection process. In other words, thecommand and control module may have a threshold number of particles or athreshold level of signal that it needs to be able to detect before itcan turn off additional sensors. Similarly, if the signal level fallsbelow a certain threshold, the command and control module may beprogrammed to turn on additional sensors to increaseintensity/sensitivity.

In one embodiment, the sensing elements may be turned off or disabledbecause they are not performing as required or otherwise have degraded.One way to do this is to run a sensing element check that includes asignal check using a pattern generator. A signal is sent to the sensorfrom an associated command and control module asking it to send apattern back. Based on the deviation of this pattern from what isexpected, the command, control and compute modules can determine theextent of damage and overall sensing element performance. If the damageis over a certain threshold, the sensor is turned off. Because thesystem has multiple redundant sensors, this typically does not effectthe overall performance of the detector.

In one embodiment, the pattern transmitted to each of the sensors todetermine its health is a chessboard pattern. In other words, everyalternate pixel is stimulated. The pattern is then read out using thecommand and control module. If a significant deviation from a chessboardpattern is received, the command and control module can determine thatthe sensor is not functioning optimally.

The command and control modules are programmed with the logic toautomatically address sensor failure. The command and control modules,for example, are programmed to seamlessly take the failing sensorsoff-line and queue up the sensors that are still operational.

In one application, for example, in an extreme operating environment outof a 1000 sensors, more than 50% may be guaranteed to fail. In suchcircumstances, the command and control module may be programmed to turnonly a certain amount of sensors on at a time. It has been determinedthat the sensors that are not operational have a greater lifetime thanoperational sensors. For example, the command and control module mayturn on 100 sensors to start with. In adverse conditions, the sensorsthat are on are more likely to fail. Accordingly, when those sensorsfail, the command and control module may have multiplexing logic to turnon the next 100 sensors and deactivate the failed sensors and continuein this fashion during the mission period of the sensor.

As noted above, pixels degrade in extreme environments. Contributingfactors include temperature, radiation, humidity etc. In one embodiment,the command and control modules or the compute modules are configured toautomatically correct for degrading pixels. In other words, degradedpixels may be characterized to determine how their electricalcharacteristics change in response to extreme environments. Thischaracterization then enables command and control modules (or computemodules) to correct for degraded pixels in real time, which allows thesensors to run continually despite being exposed to extremeenvironments.

FIGS. 32 and 34 discussed further below provide further discussionregarding the manner in which sensors can be independently controlled toensure reliability.

Power Conservation and Heat Management

Typically, the sensors will be operating in extreme environments. Thisis exacerbated by the fact that CMOS sensors heat up easily. Further,CMOS sensors get noisier as the temperature rises. Individual control ofthe sensors and redundancy allows a user to selectively turn offselective sensors, which allows for dramatic heat dissipation. In oneembodiment, the sensors can be cycled through on and off cycles toprevent heat from building up. For example, sensor 2110A and 2110B canbe turned on while sensor 2110C and 2110D is turned off and vice versa.Further, the command and control module associated with each sensor maykeep track of the sensor and its operating temperature, in oneembodiment. If the command and control module senses the temperatureexceed an acceptable threshold, it may automatically turn off thesensor.

Further, individual control over the sensors also enhances powerconservation. As noted previously, when the signal is faint in a lowparticle environment, all the sensors can be turned on. Alternatively,if the signal levels are high in a high particle environment, then allbut one or a few sensors can be turned off. This allows the system toefficiently conserve heat and energy while increasing sensor life.

In one embodiment, the sensors are configured in order to allow partialshutdown. In other words, the sensors are equipped with a mode thatallows them to be partially shut down but still operate in a low powermode. For example, in the low power mode, the CMOS sensors consumes1/1000^(th) of the maximum operating power of the CMOS. Because sensortypically utilize significant time and power to power up from an offstate, enabling a partial shut down mode allows the sensors that are notneeded to be partially shut down instead of completely turning off.These sensors can then be easily brought back online without requiringthem to go through a sensor initialization process.

FIG. 35 discussed further below contains further discussion regarding anexemplary process for conserving power and managing heat in a tunabledetector system.

Pattern Recognition and Software Extraction

As mentioned previously, in some embodiments, a discrimination processcan be executed (e.g., on the compute modules 2130A-2130D) todiscriminate between the different types of particles while minimizingany false positives. Each subatomic particle may be unique with respectto the intensity values they generate or the pattern in which theyimpinge on the pixels of pixel array P1 315. The discriminationprocedure may comprise information regarding all the particles' unique“signatures” and uses these to differentiate between particles to ensurethat false positives are not generated. The ionizing radiation-dependentsignatures are the basis for the particle discrimination algorithms thatare run on the computer modules 2130A-2130D.

For example, neutrons are observed to deposit more energy than the gammabackground (grey, black) resulting in a higher amplitude (white) pulse.Embodiments of the present invention comprise image processingprocedures (e.g., within compute modules 2130A-2130D) that use anamplitude and shape discrimination library to discriminate and identifyand count neutrons using artificial intelligence digital patternrecognition software.

As mentioned previously, embodiments of the present invention cancombine the durability and low time-to-detect of the PIN diode with thesuperior particle discrimination of the CMOS image sensor.

FIG. 23A illustrates the sensor-level measurement flow diagram and themanner in which neutron and gamma counts are output from the individualsensors and processed in accordance with an embodiment of the presentinvention.

In one embodiment, sensor information from the PIN diode sensors isanalyzed using an analog pulse neural network 2230 while sensorinformation from the CMOS sensors 2320 are analyzed using a digitalpattern neural network 2340. Neutron and gamma counts are determined inreal time and output from both types of neural networks and passedthrough a reasoning module 2350 (which may be programmed onto computemodules 2130A-2130D) to determine total neutron and gamma counts. Thereasoning model 2350 also outputs the statistical certainty of themeasurements of neutron and gamma counts in real time.

FIG. 23B illustrates is a flow diagram illustrating the manner in whichsensor information is processed and outputted by the two different typesof neural networks in accordance with an embodiment of the presentinvention. Embodiments of the present invention comprise a neural netthat is used to distinguish gamma and neutron signals in CMOS images2321 and PIN diode voltage signals 2322. In one embodiment, artificialintelligence (AI) algorithms such as Deep Fully Convolutional NueralNetwork (CNN) architectures can be used for CMOS and PIN diode sensordata processing. The neural net is pre-trained with known andexperimental data generated during sensor testing and characterization.This training process provides a trained model for fast real-timeneutron and gamma classification via pattern recognition. The PIN Diodeinput signal 2322 and the CMOS images 2321 are deconvolved to separatehigh intensity neutron signals from the low intensity gamma backgrounds.The PIN Diode input signal 2322 is deconvolved to produce output 2324from the analog pulse neural network 2330 while the CMOS input image2321 is deconvolved to produce output 2323 from the digital patternneural network 2340.

For CMOS images, the digital pattern neural network 2340 is programmedto apply the artificial intelligence procedures, e.g., Deep CNN, etc. tothe CMOS images 2321 (inputted at 60 fps from the command and controlmodules) and identify neutrons using the AI procedures that have beentrained to recognize neutron patterns in the CMOS images. The AIprocedures can, for example, be programmed to output the CMOS images ona display identifying the neutrons. Similarly, the analog pulse neuralnetwork 2330 is programmed to apply the artificial intelligenceprocedures, e.g., Deep CNN, etc. to the pulses generated by the PINdiode to determine the neutron and gamma counts.

FIG. 24A illustrates an exemplary output of a PIN diode from whichneutrons can be identified using the analog pulse neural network inaccordance with an embodiment of the present invention. Neutronsabsorbed in the converter layer of a PIN diode creates ionizingradiation. A fraction of the ionizing radiation (energy release by thenuclear reaction, alpha and beta particles, and gamma photons) incidenton the sensor is absorbed in the semiconductor active layer. Detectionin the PIN diode occurs in the active pixel area of a PIN diode. A PINdiode is a single photodiode.

An amplifier boosts the signal out of the diode by several orders ofmagnitude. The signal out of the diode, dependent on amplitude and time,is initially sorted with an analog discriminator, then fed into ananalog-to-digital (ADC) converter. Readout of the PIN diode results in aseries of time and amplitude dependent pulses. As shown in both of theimages in FIG. 24A, typically, gamma photons will produce a wider andshorter pulse 2440 than the pulse 2420 generated by neutron particles.The pulse height is determined by the amount of energy deposited by eachparticle and, typically, neutrons will deposit more energy than gammaparticles. Once the amplified pulses pass through the analogdiscriminator and ADC, the software executing on the command, controland compute modules saves and then processes the raw pulse data tofinally discriminate pulses resulting from neutron absorption frompulses resulting from gamma absorption. The resulting pulse heightinformation can also be used to determine a neutron and gamma count.Further, once the gamma and neutron counts are sorted using the analogpulse neural network 2330, the raw data is saved and input into thereasoning model 2350 for real-time, accurate particle counting.

In one embodiment, instead of using an analog pulse neural network toidentify the raw pulses from the PIN diode sensors, a digital patternneural network may be programmed to convert the images of the pulses(obtained from an oscilloscope or otherwise) in FIG. 24A to bitmapimages and train an artificial intelligence algorithm, e.g., Deep CNN toidentify and differentiate neutron related pulses 2420 from gammarelated pulses 2440 directly from the bitmap images. For example, thecommand, control and compute modules associated with a PIN diode can beprogrammed to analyze bitmap (or jpeg, png, raw, etc.) images of theneutron and gamma pulses generated by PIN diode sensors and identifyneutrons directly from the bitmap image.

FIG. 24B illustrates an exemplary output of a CMOS sensor from whichneutrons can be identified using digital pattern neural network thatanalyzes sensor information from CMOS sensors in accordance with anembodiment of the present invention. As mentioned above, neutrons areobserved to deposit more energy than the gamma background resulting in ahigher amplitude (white) pulse. The image processing software running onthe compute modules 2130A-2130D uses an amplitude and shapediscrimination library to discriminate, identify and count neutrons 2444from bitmap images generated by 60 fps by CMOS sensors usingartificial-intelligence digital pattern recognition software.

In one embodiment, CMOS pixel-level data is read out and rendered intobitmap images similar to the one shown in FIG. 24B using the digitalpattern neural network 2340. In high gamma background conditions,readout rate may be increased to reduce pileup. Neutron and gamma countsare input into the reasoning model 2350 for real-time accurate particlecounting.

As stated previously, raw data from the CMOS-based radiation sensorcomes in the form of a series of images at about 60 frames per second.Images contain 360,960 pixels of size 6.0 um×6.0 um. In an embodiment,each pixel records data in a range from 0-255.

FIG. 25A illustrates representative frames from CMOS radiation sensorsin response to varying levels of gamma radiation in accordance with anembodiment of the present invention. In an environment with no radiationsources present (0 Gy/hr), the CMOS-based radiation sensors record abackground pixel response of 7-8. Accordingly, frame 2510A appearsblack. As the gamma dose rate increases (from 0 Gy/hr to 1200 Gy/hr inimage 2520A), the overall response rises. Frames appear lighter.

FIG. 25B illustrates representative frames at the pixel level from CMOSradiation sensors in response to varying levels of gamma radiation inaccordance with an embodiment of the present invention. FIG. 25Billustrates the same frames from FIG. 25A at the pixel level. Similar toFIG. 25A, frame 2510B at 0 Gy/hr appears to be black while frame 2510Bat 1200 Gy/hr appears lighter with a higher degree of backgroundinterference.

FIG. 25C illustrates histograms of the representative images from FIGS.25A and 25B in accordance with an embodiment of the present invention.As seen in the histograms corresponding to the images of FIGS. 25A and25B, the mean response increases as dose rate increases from 7.95 at 0Gy/hr to 90.7 at 1,200 Gy/hr. Also, it is observed from the histogramsthat the standard deviation appears to change significantly at a lowdose rate. For example, standard deviation of images at 0 Gy/hr is lessthan 1 and increases by an order of magnitude at 62 Gy/hr (6.711).

In order to train artificial intelligence algorithms, e.g., Deep CNN torecognize neutron counts from images with varying levels of gammaradiation, first the neutron patterns need to be established based onpre-existing knowledge of the symmetry of neutron patterns and physicsunderlying neutron behavior. Further, the neutrons need to be labeled inthe training images based on these patterns.

FIG. 26A illustrates a collection of eight bright neutron counts with abackground gamma radiation of 0 Gy/hr in accordance with an embodimentof the present invention. The images in FIG. 26 may be a series oftraining images that are inputted into artificial intelligence softwareto train it to recognize neutrons from images with varying levels ofgamma radiation. The qualitative similarities in the patterns betweenthe neutron counts is observable from the eight images in FIG. 26. Forexample, all the counts comprise a bright and symmetric patterncharacterized by some degree of radial symmetry, saturated centerpixels, and gradually decreasing pixel intensity from the center. Eachcount appears to contain at least 4 saturated pixels 2690, where thesaturated pixel intensity equals 255. For example, the count in frame2620 comprises at least 6 saturated pixels.

FIG. 26B illustrates a magnified view of a count comprising at least 4saturated pixels. FIG. 26B illustrates the count in frame 2620 with thepixel intensity values overlaid. As seen in FIG. 26B, the count in frame2620 comprises at least 6 saturated pixels 2690.

Counts with saturated pixels are easy to identify by eye and because thecounts deposit a large amount of energy into the CMOS pixels in anidentifiable pattern, they are also identifiable by software even in thepresence of significant amounts of gamma radiation.

Once the neutrons have been labeled in training images based on thepatterns, the deep learning artificial neural network, e.g., Deep CNNcan be trained to recognize these patterns. Other types of artificialintelligence algorithms that can be trained to recognize neutrons inimages include for example ReLu CNN, Cascaded CNN, Support VectorMachine, Random Forest, XG Boost, LSTM of various kinds, RecurrentNeural Networks, Convolution Deep Neural Networks, and Bayesian DeepNeural Networks. After the deep learning software is trained, thesoftware can be tested on a new set of images where neutrons have beenpreviously been identified. In other words, the deep learning softwareis tested on images where the number and location of the neutrons isknown. If the accuracy is acceptable, then new images may be fed to theartificial neural networks. Otherwise, training is conducted with moreimages. In one embodiment, if the deep learning software flags falsepositives, it can be re-trained to recognize the patterns that result infalse positives and refrain from flagging them in the future.

FIG. 27A illustrates a first pixel level image with neutron and gammasignatures in the same image in accordance with an embodiment of thepresent invention. In one embodiment, the detector of the presentinvention detects gamma radiation and neutrons within a single frame.Neutron and gamma signatures are easily discriminated due to a widedifference in physical interactions. Neutron signatures deposit asignificant amount of energy into the CMOS pixels and retain theirdistinct size and shape patterns even as gamma interactions increase theambient image brightness.

The image in FIG. 27A contains a relatively low level of backgroundgamma radiation, approximately, 62 Gy/hr. Accordingly, the neutronpattern 2702 is easily distinguishable The image in FIG. 27 is anexample of neutron detection in an environment with neutron flux andgamma radiation present. The image contains one bright neutron count anda distinct signature of a gamma photon 2704.

As mentioned previously, neutrons interact with the converter layeradjacent to the CMOS pixel. Reaction between the neutron and converterlayer produce an alpha particle and triton, ionizing particles whichhave a finite interaction probability with silicon. Gamma photonsinteract directly with silicon via Compton scattering, depositing somefraction of their energy into the crystal lattice. Change in voltage asa result of the presence of charge in the CMOS active-pixel is measured.

It should be noted that embodiments of the present invention may also beused to detect gamma photons using, e.g., the CMOS sensors. As noted,the gamma photons interact with the silicon and illuminate pixels asshown in FIG. 27A with a distinct gamma signature 2704.

Neutron counts in the image of FIG. 27A are readily identifiable becausethey comprises a characteristic bright and symmetric pattern with somedegree of radial symmetry, saturated center pixels, and graduallydecreasing pixel intensity from the center. Further, as compared withthe pixel intensity of the neighboring gamma photon 2704, the maximumpixel intensity of the neutron count is much higher. For example, inFIG. 27A, the maximum pixel intensity deposited by the gamma photonscattering interaction is 87, with a total of 200-300 pixel intensityfor the six brightest pixels in the gamma track. By comparison, themaximum pixel intensity for the neutron count is 255 (saturation) withthe bright pixels summing up to more than 3000.

In one embodiment, the command and control module can be programmed torecognize damaged pixels within a sensor. Damaged sensors can bedetected because they will typically be stuck at high intensity valuesfor prolonged periods. For example, a damaged pixel may provide aconsistent reading of 255 for several consecutive frames. The commandand control module can be programmed to turn off certain pixelsselectively within the sensor. In other words, at the software level,the command and control module can be programmed to recognize thatcertain pixels are damaged and to ignore the output from those pixels.

FIG. 27B illustrates a second pixel level image with neutron and gammasignatures in the same image in accordance with an embodiment of thepresent invention. As seen in FIG. 27B, the neutron count 2780 isreadily identifiable because it comprises a characteristic bright andsymmetric pattern with some degree of radial symmetry, saturated centerpixels, and gradually decreasing pixel intensity from the center.Meanwhile, the gamma patterns 2750 and 2730 can be easily distinguishedfrom the neutron pattern because the gamma patterns are dimmer, havemuch lower cumulative pixel intensity, do not contain any saturatedpixels, and the patterns do not tend to be symmetric. Asymmetricpatterns generally tend to be not related to neutrons. Because of thestark differences in features between the gamma and neutron patterns atthe pixel level, the deep learning software can be easily trained todistinguish between the two types of pattern.

FIG. 28 illustrates pixel level images with neutron counts under highgamma conditions in accordance with an embodiment of the presentinvention. The pixel level images shown in FIG. 28 each contain a brightneutron count 2890 under 1,000 Gy/hr gamma conditions. As seen in FIG.28, at 1,000 Gy/hr the bright count 2890 in each frame is less visiblethan at 62 Gy/hr but still reliably distinguishable by shape (size andsymmetry) and intensity. Accordingly, the deep learning algorithms caneasily be trained to recognize bright neutron counts even in images witha high degree of gamma radiation, e.g., 1,200 Gy/hr.

FIG. 29 depicts a flowchart 2900 of an exemplary computer implementedprocess for detecting the presence of neutrons in images produced fromsensor information in accordance with an embodiment of the presentinvention.

At step 2902, the neutron patterns first need to be established based onprior knowledge of the physics of neutron behavior and otherinformation, for example, the known symmetrical behavior of neutroncounts.

At step 2904, a collection of training images containing known neutronpatterns in them are labeled. In other words, the neutron pattern in thetraining images is confirmed and the pixels that are part of the neutronpattern are clearly labeled.

At step 2906, the deep learning software, e.g., Deep CNN is trained torecognize the known patterns using the labeled training images.

At step 2908, the artificial intelligence software is tested on a newset of images where the presence or absence of neutrons is known. Forexample, a number of images known to either contain or be free ofneutrons are fed to the artificial intelligence software.

If the accuracy is over a certain threshold at step 2910, then newimages are ready to be tested using the deep learning software at step2910. If the accuracy is not satisfactory, then the software needs to bere-trained with further images at step 2912.

As mentioned earlier, in one embodiment, the deep learning software canbe programmed to execute on the computer modules 2130A-2130D shown inFIG. 21. In one embodiment, once the artificial intelligence softwaredetects a neutron, information received from the command and controlmodules can be used to precisely identify the sensor number, the framenumber, the time-stamp and the x-y coordinates of the pixel where theneutron was detected. In one embodiment, the information is displayedalong with the image containing the neutron pattern on display 2150 forthe user to view.

In one embodiment, the software running on the compute modules can alsobe programmed to combine the neutron detection information from varioussensors, e.g., all the sensors arranged in a cubical configuration asshown in FIG. 13, to determine the source of the neutrons using thetriangulation methods discussed above. Further, in an application suchas the damaged nuclear reactor discussed above, the robot sent into thecontainment units to do a scan (equipped, e.g., with the exemplarydetector unit from FIG. 14) can be programmed to operate in autonomousdriving mode and use the information from the triangulation toindependently locate the source of the neutrons. In one embodiment,machine learning can also be used to teach the robot to independentlyfind the source of neutrons within the containment units. The robot canalso use the information collected from the various sensors to generatea 3D debris map based on the detected neutrons.

FIG. 30 depicts a flowchart 3000 of an exemplary computer implementedprocess for analyzing images to detect neutrons using deep learningprocesses in accordance with an embodiment of the present invention.

At step 3002, an input image is split into multiple frames or patches.For example, given an input image of certain dimensions (e.g.,752×480px) captured from the sensor, the software running on computemodules 2130A-2130D first splits the image into frames or patches (e.g.,48×48 or 96×96 pixels).

At step 3004, these patches are passed through the trained Deep Learningmodel (e.g., Deep CNN) with a forward pass.

At step 3006, a probability value is received from the deep learningprocesses. In other words, the output from the Deep CNN is theprobability of each image pixel being a neutron. In one embodiment, apredetermined threshold value can be used over which the pixels areconsidered as being neutron pixels. For example, over a predeterminedthreshold of 75% probability, a pixel can be considered as being aneutron pixel. Thereafter, if the Deep CNN indicates that a pixel isover the 75% threshold probability value, the pixel is considered asbeing a neutron related pixel.

At step 3008, the image is stitched back into the original imagedimensions.

At step 3010, the total number of neutrons present in the stitched imageare counted using components connected to each pixel. As seen, forexample, in FIGS. 26A, 26B and 27A, a neutron is identified by not onlyconsidering a saturated pixel or a high intensity pixel, but also byexamining the group of pixels around the high intensity pixel.Accordingly, in order to identify a neutron, all the pixels adjacent tothe pixel identified as being over the threshold level of probabilityare examined. The connected components examined will typically beadjacent to, share a vertex, or share a common boundary with theidentified pixel of interest. Further, any pixels that have relativelyhigher intensities and identified as a connected component may beexamined. By examining the features of the pixel of interest along withthe connected pixels, the deep learning process is able to determinewhether the cumulative pattern formed by the pixel of interest and theconnected pixels is associated with neutrons or gammas.

At step 3012, a neutron location is determined using a binary mask. Abinary mask is a control image with the same number of pixels as theimage that is run through the deep learning process. However, in abinary mask, a zero may be assigned to all pixels that do not have anypart of the neutron generated pattern, and a 1 may be assigned to allpixels with any part of the neutron generated pattern. The binary mask,once created, can be used to extract all the neutrons from an image byconvolving (pixel by pixel multiplication) it with the original image.

At step 3014, the gamma flux in the image is measured using a summationof pixels. As discussed in connection with FIG. 27A, one way gammaradiation can be distinguished from neutrons is by summing up pixelintensities. Gamma radiation will typically exhibit lower cumulativepixel intensities in a pattern than in a neutron pattern. As discussedin connection with FIG. 27A, summing up pixel intensities involvessumming up the intensity values of each pixel that is part of thepattern. Each pixel in a CMOS sensor has an intensity value that rangesfrom 0 to 255 (for 8 bit CMOS sensor) or 0 to 1024 for (10 bit CMOSsensor) etc. When gamma radiation is incident on the pixels, charges getgenerated as a result of Compton scattering (interaction of high energygamma photons with Silicon). Note that gamma dose rate can be measuredreliably by measuring the extent of energy deposited on the pixels—thistypically translates to a convolution or summation of the pixelintensities that the sensor generates in response to an independentlycalibration and measured gamma field.

Visible light which has photons such as green photons will typicallycause silicon electrons to get excited and move to the conduction band.Gamma photons contain much more energy than green photons, e.g., amillion times more. When gamma photons enter the silicon CMOS sensorpixel, they might pass through without interacting. However, if they dointeract, they will dislodge an electron from the atom itself. The gammaphotons excited the silicon electrons to an extent that the electronsmove beyond the condition band and get dislodged from the atom itself.These energetic fast electrons are responsible for the charges that areobserved on the CMOS sensor in gamma related patterns.

Since the CMOS sensor is an excellent charge detection device, itregisters the charges produced in each pixel as an “intensity” valuethat is a measure of how many electrons have been collected in thepixel. If the electrons fill up the pixel, it is possible that they willspill over by diffusion to adjacent pixels. The more the gamma photonsthat are incident on the silicon, the more charges get produced and themore the intensity observed in the pixels that see such interaction.Accordingly, one can derive a linear calibration regime for gammaincident dose, where the dose is the amount of energy that getsdeposited in the silicon. Higher intensity values for pixels in a gammapattern simply means a higher dose of gamma radiation. From theintensity values of the pixels in a gamma pattern, the gamma flux can bedetermined. As noted above, embodiments of the present invention, areable to detect neutrons in environments where the gamma flux is as highas 1,200 Gy/hr.

At step 3016, the binary mask can also be multiplied with the inputimage to determine neutron intensity. The multiplication is simply apixel by pixel multiplication of two images. In other words, it is theelement by element multiplication of two matrices—one of which has theintensity values in it (the original image) and the other has 1s and 0s(the binary mask).

Finally, at step 3018, statistics are generated for each input frame andare written into a file to be used for later analysis.

FIG. 31 depicts a flowchart 3100 of an exemplary computer implementedprocess for triangulating a source location for neutron particles inaccordance with an embodiment of the present invention. It should benoted that while FIG. 31 is discussed in the context of neutrons,similar techniques may be applied to triangulate sources of any particleof interest to the user of a detector.

At step 3101, the sensing elements in a detector are configuredgeometrically in various different ways to scan an area in order tolocate for sources of radiation. For example, the sensing elements orsensors can be arranged around a cube, cuboid, sphere, icosahedron, etc.Each of these configurations is reminiscent of a compound “eye” that isscanning some or all directions looking for neutrons and other subatomicparticles.

At step 3102, an inspection is conducted of the area under investigationusing the detector. In other words, the detector is used to locateparticles of interest in the area suspected to contain the source of theradiation.

At step 3103, the information is extracted from the sensors in thedetector and analyzed using software tools in the command, control andcompute modules shown in FIG. 21. The pattern of impingement of theneutrons on the detector, for example, may be analyzed by the software.For instance, as discussed above in connection with FIGS. 12 and 13, ifthe detector is a cubed detector, then the patterns of neutronimpingement on each sensor on each face of the cube may be analyzed.

As mentioned previously, the neutrons incident on one face of the cubewill largely be detected by the sensing elements on that face. Since thesensing elements can be pixelated, with each pixel serving as adetection element, the angle between a sensing element and the sourcealso creates a gradient of detection within the pixels. Within the samesensing element (e.g., the same SPU), the pixels closest to the sourcewill likely detect more neutrons than the pixels farther away. By usingthe counts and profiles of neutrons detected on each side of the cube,it becomes possible to determine the location of the source bytriangulating the results from all the elements on different sides ofthe cube.

Accordingly, at step 3104, the radiation source can be determined bytriangulating results from all the sensors on the plurality of surfacesof the detector.

FIG. 32 depicts a flowchart 3200 of an exemplary computer implementedprocess for independently controlling sensors in order to ensurereliability in accordance with an embodiment of the present invention.

At step 3201, data is received at one or more command and controlmodules 2120A-2120D from a plurality of sensors in a detector.

At step 3202, based on the data received, the command and controlmodules can determine the number of particles of interest in theenvironment being detected. For example, if a high number of neutronsare detected in a nuclear reactor, the command and control modules candetermine that the signal level is high. Alternatively, the command andcontrol modules can also use the particle count to determine if thesignal level is low.

At step 3203, in response to a determination that the signal level ishigh, one or more sensors in the detector are turned off. Since multipleredundant sensors are used in the detector to ensure reliability, thisdoes not affect the overall performance of the detector. Similarly, atstep 3204, in response to a determination that the signal level is low,some of the previously turned off redundant sensors can be turned backon again.

FIG. 33 depicts a flowchart 3300 of an exemplary computer implementedprocess for gathering information from tunable sensors used for particledetection in accordance with an embodiment of the present invention.

At step 3301, information is extracted from one or more tunable sensorsused for particle detection. In one embodiment, the sensors can be in anadverse environment. At step 3302, the data from the sensors isserialized so that it can transmitted over long distances using thincables, e.g., cables that are less than or equal to 3 mm in diameter. Inone embodiment, the data from the sensor can also be tagged with the IDnumber of the sensor and a timestamp prior to transmitting.

At step 3303, the data is transmitted over the cables at MHz to GHzfrequencies. In one embodiment, the thin cables can be twisted paircables, coaxial cables or optical fiber cables. In another embodiment,the thin cables comprise protective shielding in order to make sure theycan endure the adverse environmental conditions.

At step 3304, the serialized data can be received at the command andcontrol modules 2120A-2120D where the serialized data can be convertedinto a sequence of images at the rate of 60 frames per second.

Finally, at step 3305, the compute modules 2130A-2130D can be used toexecute particle discrimination procedures, e.g., using deep learningprocesses, on the sequence of images to detect the presence of particlesof interest, e.g., gamma photons, neutrons, etc.

FIG. 34 depicts a flowchart 3400 of an exemplary computer implementedprocess for disabling sensors that are not functioning in order toensure reliability of the detector and increases the operational life ofthe detector in accordance with an embodiment of the present invention.

At step 3401, a pattern is transmitted from the command and controlmodules 2120A-2120D to a plurality of sensors. For example, in oneembodiment, a chessboard pattern can be transmitted to the plurality ofsensors.

At step 3402, the pattern is received back from each of the plurality ofsensors at the command and control module.

At step 3403, the received patterns are examined and compared with thepatterns that were transmitted in order to determine a deviation betweenthe two for each of the sensor modules.

At step 3404, responsive to a determination that the deviation is abovea predetermined threshold for a particular sensor, the malfunctioningsensor is taken offline. In other words, the command and control modulehas individual control over the sensors and can deactivatemalfunctioning sensors.

Further, at step 3405, a previously deactivated sensor that was turnedoff (for power related or other reasons) but is still operational can beturned back on to replace the sensor that was taken offline.

FIG. 35 depicts a flowchart 3500 of an exemplary computer implementedprocess for conserving power and managing heat in a tunable detectorsystem in accordance with an embodiment of the present invention.

At step 3501, temperature readings are received at the command andcontrol modules 2120A-2120D from a plurality of sensors inside adetector.

At step 3502, the command and control modules determine if thetemperature of any of the sensors has exceeded a predeterminedthreshold.

At step 3503, the sensors, where the temperature has exceeded thethreshold values, are placed in partial shutdown mode. If the partialshutdown mode is unavailable, then the sensors are completely shut off.

At step 3504, the sensors that were shut down are cycled back on after apredetermined amount of time has elapsed. In this way, power isconserved and heat is managed by cycling sensors on and off as neededbased on the temperature readings from the sensors.

While the foregoing disclosure sets forth various embodiments usingspecific block diagrams, flowcharts, and examples, each block diagramcomponent, flowchart step, operation, and/or component described and/orillustrated herein may be implemented, individually and/or collectively,using a wide range of hardware, software, or firmware (or anycombination thereof) configurations. In addition, any disclosure ofcomponents contained within other components should be considered asexamples because many other architectures can be implemented to achievethe same functionality.

The process parameters and sequence of steps described and/orillustrated herein are given by way of example only. For example, whilethe steps illustrated and/or described herein may be shown or discussedin a particular order, these steps do not necessarily need to beperformed in the order illustrated or discussed. The various examplemethods described and/or illustrated herein may also omit one or more ofthe steps described or illustrated herein or include additional steps inaddition to those disclosed.

While various embodiments have been described and/or illustrated hereinin the context of fully functional computing systems, one or more ofthese example embodiments may be distributed as a program product in avariety of forms, regardless of the particular type of computer-readablemedia used to actually carry out the distribution. The embodimentsdisclosed herein may also be implemented using software modules thatperform certain tasks. These software modules may include script, batch,or other executable files that may be stored on a computer-readablestorage medium or in a computing system. These software modules mayconfigure a computing system to perform one or more of the exampleembodiments disclosed herein. One or more of the software modulesdisclosed herein may be implemented in a cloud computing environment.Cloud computing environments may provide various services andapplications via the Internet. These cloud-based services (e.g.,software as a service, platform as a service, infrastructure as aservice, etc.) may be accessible through a Web browser or other remoteinterface. Various functions described herein may be provided through aremote desktop environment or any other cloud-based computingenvironment.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as may be suited to theparticular use contemplated.

Embodiments according to the invention are thus described. While thepresent disclosure has been described in particular embodiments, itshould be appreciated that the invention should not be construed aslimited by such embodiments, but rather construed according to the belowclaims.

What is claimed is:
 1. A method of capturing and analyzing information for a particle detection system, the method comprising: generating a reaction to a plurality of particles using a converter material, wherein the converter material is operable to interact with the plurality of particles; converting a response to the reaction to an electrical signal using a plurality of sensors, wherein the converter material is operable to be coated onto the plurality of sensors, and wherein each sensor of the plurality of sensors comprises an array of discrete pixel sensors, each discrete pixel sensor with a respective (x,y) coordinate within the array of discrete pixel sensors; processing the electrical signal to generate data regarding each discrete pixel sensor in the array of discrete pixel sensors; serializing the data collected from the plurality of sensors and transmitting the data over thin cables to a processing unit, wherein the processing unit is located at a separate and remote location from the plurality of sensors; and converting the data into a sequence of images comprising a visual representation of the plurality of particles impinging on the plurality of sensors.
 2. The method of claim 1, further comprising: displaying the sequence of images on a display; and executing a discrimination procedure using the sequence of images that uses known unique radiation-dependent signature patterns created by various types of particles to discriminate between the plurality of particles impinging on the plurality of sensors.
 3. The method of claim 1, wherein a diameter associated with each of the thin cables is less than or equal to 3 mm.
 4. The method of claim 1, wherein the transmitting the data comprises transmitting the data over distances longer than 60 meters.
 5. The method of claim 1, wherein the plurality of particles comprises neutrons.
 6. The method of claim 1, wherein the serializing the data comprises serializing the data at megahertz frequencies.
 7. The method of claim 1, wherein the serializing the data comprises serializing the data at gigahertz frequencies.
 8. The method of claim 1, wherein the transmitting the data comprises transmitting the data using low-voltage differential signaling (LVDS).
 9. The method of claim 1, wherein the thin cables are selected from a group consisting of: twisted pair cables, coaxial cables, and optical fiber cables.
 10. The method of claim 1, wherein the serializing the data comprises: attaching a timestamp and sensor identification information to the data prior to the transmitting, wherein the timestamp and sensor identification information are operable to be used by the processing unit to distinguish between data from the plurality of sensors.
 11. The method of claim 1, wherein the thin cables are coated with a protective shielding, wherein a material for the protective shielding is selected from a group consisting of: lead, tungsten, boron, and gadolinium.
 12. An apparatus for capturing and analyzing information for a particle detection system, said apparatus comprising: a plurality of converter layers operable to interact with and generate a reaction to a plurality of particles, wherein the plurality of particles comprise neutrons; a plurality of sensors, each sensor of the plurality of sensors in proximity to and facing a respective converter layer from the plurality of converter layers, wherein the plurality of sensors are operable to convert a response to the reaction to an electrical signal, and wherein each sensor of the plurality of sensors comprises an array of discrete pixel sensors; a first processing device operable to process the electrical signal to generate data for each discrete pixel sensor on the array of discrete pixel sensors; a data serializer to serialize the data generated; a second processing device; transmission line cables for transmitting the data to the second processing device, wherein the second processing device is located at a separate location from the plurality of sensors; and wherein the second processing device communicatively coupled to the first processing device and configured to: control the first processing device; receive the data from the first processing device; and convert the data into a sequence of images comprising a visual representation of the plurality of particles impinging on the plurality of sensors.
 13. The apparatus of claim 12, wherein the transmission line cables are operable to transmit the data pertaining to each sensor of the plurality of sensors at a different frequency.
 14. The apparatus of claim 13, wherein the second processing device is operable to read out the data for each sensor of the plurality of sensors transmitted at different frequencies and consolidate the data regarding each sensor of the plurality of sensors to develop the sequence of images.
 15. A system for detecting neutrons, said system comprising: a plurality of sensor arrays, wherein each sensor array of the plurality of sensor arrays comprises a plurality of sensors, wherein each sensor of the plurality of sensors comprises: a converter layer disposed thereon and operable to interact with and generate a reaction to a plurality of particles, wherein the plurality of particles comprises neutrons; an array of discrete pixel sensors, each discrete pixel sensor with a respective (x,y) coordinate within the array of discrete pixel sensors, wherein the array of discrete pixel sensors is operable to convert a response to the reaction to a readable electrical signal; and a first processing device operable to process the readable electrical signal to generate data for each discrete pixel sensor on the array of discrete pixel sensors; and a plurality of data serializers to serialize the data, wherein each data serializer from the plurality of data serializers is associated with a sensor of the plurality of sensors; thin cables for data transmission; and a plurality of second processing devices communicatively coupled to the plurality of sensors, wherein each second processing device of the plurality of second processing devices is associated with a sensor of the plurality of sensors, wherein each second processing device is operable to receive the serialized data from an associated sensor of the plurality of sensors and transmitted over the thin cables, and wherein the plurality of second processing devices are located at a remote location relative to the plurality of sensors arrays.
 16. The system of claim 15, wherein each second processing device of the plurality of second processing devices is configured to: control a respective first processing device associated with a sensor of the plurality of sensors; receive the data from the respective first processing device through a respective data serializer associated with a data serializer of the plurality of data serializers; and convert the data into a sequence of images comprising a visual representation of the plurality of particles impinging on an associated sensor of the plurality of sensors.
 17. The system of claim 15, wherein the plurality of sensor arrays are stacked adjacent to each other to increase a sensitivity.
 18. The system of claim 15, wherein the plurality of sensor arrays are arranged in a cubical configuration, wherein each face of the cubical configuration comprises a plurality of stacked sensor arrays adjacent to each other.
 19. The system of claim 15, wherein the plurality of sensor arrays are arranged in a configuration selected from the group consisting of: a cube, a cuboid, a sphere, and a icosahedron.
 20. A system for detecting neutrons, said system comprising: a plurality of sensor arrays, wherein each sensor array of the plurality of sensor arrays comprises a plurality of sensors, wherein each sensor of the plurality of sensors comprises: a converter layer disposed thereon and operable to interact with and generate a reaction to a plurality of particles, wherein the plurality of particles comprises neutrons; an array of discrete pixel sensors, each discrete pixel sensor with a respective (x,y) coordinate within the array of discrete pixel sensors, wherein the array of discrete pixel sensors is operable to convert a response to the reaction to a readable electrical signal; and a first processing device operable to process the readable electrical signal to generate data for each discrete pixel sensor on the array of discrete pixel sensors; and a data serializer to serialize the data, wherein the first processing device and the data serializer are located in proximity to a respective sensor of the plurality of sensors; thin cables for data transmission; and a plurality of second processing devices communicatively coupled to the plurality of sensors, wherein each second processing device of the plurality of second processing devices is associated with a sensor of the plurality of sensors, wherein each second processing device is operable to receive the serialized data from an associated sensor of the plurality of sensors wherein the serialized data is transmitted over the thin cables, wherein the plurality of second processing devices are located at a separate and remote location from the plurality of sensors arrays, and wherein the plurality of second processing device are operable to detect a particle type based on the serialized data received from the plurality of sensor arrays. 